parent_paper_title
stringclasses 63
values | parent_paper_arxiv_id
stringclasses 63
values | citation_shorthand
stringlengths 2
56
| raw_citation_text
stringlengths 9
63
| cited_paper_title
stringlengths 5
161
| cited_paper_arxiv_link
stringlengths 32
37
⌀ | cited_paper_abstract
stringlengths 406
1.92k
⌀ | has_metadata
bool 1
class | is_arxiv_paper
bool 2
classes | bib_paper_authors
stringlengths 2
2.44k
⌀ | bib_paper_year
float64 1.97k
2.03k
⌀ | bib_paper_month
stringclasses 16
values | bib_paper_url
stringlengths 20
116
⌀ | bib_paper_doi
stringclasses 269
values | bib_paper_journal
stringlengths 3
148
⌀ | original_title
stringlengths 5
161
| search_res_title
stringlengths 4
122
| search_res_url
stringlengths 22
267
| search_res_content
stringlengths 19
1.92k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Aligning Web Query Generation with Ranking Objectives via Direct
Preference Optimization
|
2505.19307v1
|
DBLP:journals/corr/abs-2202-05144
|
\cite{DBLP:journals/corr/abs-2202-05144}
|
InPars: Data Augmentation for Information Retrieval using Large Language
Models
|
http://arxiv.org/abs/2202.05144v1
|
The information retrieval community has recently witnessed a revolution due
to large pretrained transformer models. Another key ingredient for this
revolution was the MS MARCO dataset, whose scale and diversity has enabled
zero-shot transfer learning to various tasks. However, not all IR tasks and
domains can benefit from one single dataset equally. Extensive research in
various NLP tasks has shown that using domain-specific training data, as
opposed to a general-purpose one, improves the performance of neural models. In
this work, we harness the few-shot capabilities of large pretrained language
models as synthetic data generators for IR tasks. We show that models finetuned
solely on our unsupervised dataset outperform strong baselines such as BM25 as
well as recently proposed self-supervised dense retrieval methods. Furthermore,
retrievers finetuned on both supervised and our synthetic data achieve better
zero-shot transfer than models finetuned only on supervised data. Code, models,
and data are available at https://github.com/zetaalphavector/inpars .
| true | true |
Luiz Henrique Bonifacio and
Hugo Abonizio and
Marzieh Fadaee and
Rodrigo Frassetto Nogueira
| 2,022 | null | null | null |
ArXiv
|
InPars: Data Augmentation for Information Retrieval using Large Language
Models
|
InPars: Data Augmentation for Information Retrieval using Large ...
|
https://arxiv.org/abs/2202.05144
|
In this work, we harness the few-shot capabilities of large pretrained language models as synthetic data generators for IR tasks.
|
Aligning Web Query Generation with Ranking Objectives via Direct
Preference Optimization
|
2505.19307v1
|
DBLP:journals/corr/abs-2301-01820
|
\cite{DBLP:journals/corr/abs-2301-01820}
|
{InPars-v2: Large Language Models as Efficient Dataset Generators for
Information Retrieval}
| null | null | true | false |
Vitor Jeronymo and
Luiz Henrique Bonifacio and
Hugo Abonizio and
Marzieh Fadaee and
Roberto de Alencar Lotufo and
Jakub Zavrel and
Rodrigo Frassetto Nogueira
| 2,023 | null | null | null |
ArXiv
|
{InPars-v2: Large Language Models as Efficient Dataset Generators for
Information Retrieval}
|
(PDF) InPars-v2: Large Language Models as Efficient Dataset ...
|
https://www.researchgate.net/publication/366902520_InPars-v2_Large_Language_Models_as_Efficient_Dataset_Generators_for_Information_Retrieval
|
(PDF) InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents. In this work we introduce InPars-v2, a dataset generator that uses open-source LLMs and existing powerful rerankers to select synthetic query-document pairs for training. A simple BM25 retrieval pipeline followed by a monoT5 reranker finetuned on InPars-v2 data achieves new state-of-the-art results on the BEIR benchmark. We also made available all the synthetic data generated in this work for the 18 different datasets in the BEIR benchmark which took more than 2,000 GPU hours to be generated as well as the reranker models finetuned on the synthetic data.
|
Aligning Web Query Generation with Ranking Objectives via Direct
Preference Optimization
|
2505.19307v1
|
DBLP:conf/iclr/DaiZMLNLBGHC23
|
\cite{DBLP:conf/iclr/DaiZMLNLBGHC23}
|
Promptagator: Few-shot Dense Retrieval From 8 Examples
|
http://arxiv.org/abs/2209.11755v1
|
Much recent research on information retrieval has focused on how to transfer
from one task (typically with abundant supervised data) to various other tasks
where supervision is limited, with the implicit assumption that it is possible
to generalize from one task to all the rest. However, this overlooks the fact
that there are many diverse and unique retrieval tasks, each targeting
different search intents, queries, and search domains. In this paper, we
suggest to work on Few-shot Dense Retrieval, a setting where each task comes
with a short description and a few examples. To amplify the power of a few
examples, we propose Prompt-base Query Generation for Retriever (Promptagator),
which leverages large language models (LLM) as a few-shot query generator, and
creates task-specific retrievers based on the generated data. Powered by LLM's
generalization ability, Promptagator makes it possible to create task-specific
end-to-end retrievers solely based on a few examples {without} using Natural
Questions or MS MARCO to train %question generators or dual encoders.
Surprisingly, LLM prompting with no more than 8 examples allows dual encoders
to outperform heavily engineered models trained on MS MARCO like ColBERT v2 by
more than 1.2 nDCG on average on 11 retrieval sets. Further training
standard-size re-rankers using the same generated data yields another 5.0 point
nDCG improvement. Our studies determine that query generation can be far more
effective than previously observed, especially when a small amount of
task-specific knowledge is given.
| true | true |
Zhuyun Dai and
Vincent Y. Zhao and
Ji Ma and
Yi Luan and
Jianmo Ni and
Jing Lu and
Anton Bakalov and
Kelvin Guu and
Keith B. Hall and
Ming{-}Wei Chang
| 2,023 | null | null | null | null |
Promptagator: Few-shot Dense Retrieval From 8 Examples
|
Promptagator: Few-shot Dense Retrieval From 8 Examples
|
https://openreview.net/forum?id=gmL46YMpu2J
|
In this paper, we suggest to work on Few-shot Dense Retrieval, a setting where each task comes with a short description and a few examples.
|
Aligning Web Query Generation with Ranking Objectives via Direct
Preference Optimization
|
2505.19307v1
|
DBLP:journals/corr/abs-2403-20327
|
\cite{DBLP:journals/corr/abs-2403-20327}
|
Gecko: Versatile Text Embeddings Distilled from Large Language Models
|
http://arxiv.org/abs/2403.20327v1
|
We present Gecko, a compact and versatile text embedding model. Gecko
achieves strong retrieval performance by leveraging a key idea: distilling
knowledge from large language models (LLMs) into a retriever. Our two-step
distillation process begins with generating diverse, synthetic paired data
using an LLM. Next, we further refine the data quality by retrieving a set of
candidate passages for each query, and relabeling the positive and hard
negative passages using the same LLM. The effectiveness of our approach is
demonstrated by the compactness of the Gecko. On the Massive Text Embedding
Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing
entries with 768 embedding size. Gecko with 768 embedding dimensions achieves
an average score of 66.31, competing with 7x larger models and 5x higher
dimensional embeddings.
| true | true |
Jinhyuk Lee and
Zhuyun Dai and
Xiaoqi Ren and
Blair Chen and
Daniel Cer and
Jeremy R. Cole and
Kai Hui and
Michael Boratko and
Rajvi Kapadia and
Wen Ding and
Yi Luan and
Sai Meher Karthik Duddu and
Gustavo Hern{\'{a}}ndez {\'{A}}brego and
Weiqiang Shi and
Nithi Gupta and
Aditya Kusupati and
Prateek Jain and
Siddhartha Reddy Jonnalagadda and
Ming{-}Wei Chang and
Iftekhar Naim
| 2,024 | null | null | null |
ArXiv
|
Gecko: Versatile Text Embeddings Distilled from Large Language Models
|
Gecko: Versatile Text Embeddings Distilled from Large Language Models
|
http://arxiv.org/pdf/2403.20327v1
|
We present Gecko, a compact and versatile text embedding model. Gecko
achieves strong retrieval performance by leveraging a key idea: distilling
knowledge from large language models (LLMs) into a retriever. Our two-step
distillation process begins with generating diverse, synthetic paired data
using an LLM. Next, we further refine the data quality by retrieving a set of
candidate passages for each query, and relabeling the positive and hard
negative passages using the same LLM. The effectiveness of our approach is
demonstrated by the compactness of the Gecko. On the Massive Text Embedding
Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing
entries with 768 embedding size. Gecko with 768 embedding dimensions achieves
an average score of 66.31, competing with 7x larger models and 5x higher
dimensional embeddings.
|
Aligning Web Query Generation with Ranking Objectives via Direct
Preference Optimization
|
2505.19307v1
|
DBLP:journals/corr/abs-2411-00722
|
\cite{DBLP:journals/corr/abs-2411-00722}
|
Token-level Proximal Policy Optimization for Query Generation
|
http://arxiv.org/abs/2411.00722v1
|
Query generation is a critical task for web search engines (e.g. Google,
Bing) and recommendation systems. Recently, state-of-the-art query generation
methods leverage Large Language Models (LLMs) for their strong capabilities in
context understanding and text generation. However, they still face challenges
in generating high-quality queries in terms of inferring user intent based on
their web search interaction history. In this paper, we propose Token-level
Proximal Policy Optimization (TPPO), a noval approach designed to empower LLMs
perform better in query generation through fine-tuning. TPPO is based on the
Reinforcement Learning from AI Feedback (RLAIF) paradigm, consisting of a
token-level reward model and a token-level proximal policy optimization module
to address the sparse reward challenge in traditional RLAIF frameworks. To
evaluate the effectiveness and robustness of TPPO, we conducted experiments on
both open-source dataset and an industrial dataset that was collected from a
globally-used search engine. The experimental results demonstrate that TPPO
significantly improves the performance of query generation for LLMs and
outperforms its existing competitors.
| true | true |
Yichen Ouyang and
Lu Wang and
Fangkai Yang and
Pu Zhao and
Chenghua Huang and
Jianfeng Liu and
Bochen Pang and
Yaming Yang and
Yuefeng Zhan and
Hao Sun and
Qingwei Lin and
Saravan Rajmohan and
Weiwei Deng and
Dongmei Zhang and
Feng Sun and
Qi Zhang
| 2,024 | null | null | null |
ArXiv
|
Token-level Proximal Policy Optimization for Query Generation
|
Token-level Proximal Policy Optimization for Query Generation
|
https://www.researchgate.net/publication/385510091_Token-level_Proximal_Policy_Optimization_for_Query_Generation
|
In this paper, we propose Token-level Proximal Policy Optimization (TPPO), a noval approach designed to empower LLMs perform better in query generation through
|
Aligning Web Query Generation with Ranking Objectives via Direct
Preference Optimization
|
2505.19307v1
|
DBLP:journals/corr/SchulmanWDRK17
|
\cite{DBLP:journals/corr/SchulmanWDRK17}
|
Proximal Policy Optimization Algorithms
|
http://arxiv.org/abs/1707.06347v2
|
We propose a new family of policy gradient methods for reinforcement
learning, which alternate between sampling data through interaction with the
environment, and optimizing a "surrogate" objective function using stochastic
gradient ascent. Whereas standard policy gradient methods perform one gradient
update per data sample, we propose a novel objective function that enables
multiple epochs of minibatch updates. The new methods, which we call proximal
policy optimization (PPO), have some of the benefits of trust region policy
optimization (TRPO), but they are much simpler to implement, more general, and
have better sample complexity (empirically). Our experiments test PPO on a
collection of benchmark tasks, including simulated robotic locomotion and Atari
game playing, and we show that PPO outperforms other online policy gradient
methods, and overall strikes a favorable balance between sample complexity,
simplicity, and wall-time.
| true | true |
John Schulman and
Filip Wolski and
Prafulla Dhariwal and
Alec Radford and
Oleg Klimov
| 2,017 | null | null | null |
ArXiv
|
Proximal Policy Optimization Algorithms
|
Proximal Policy Optimization Algorithms
|
http://arxiv.org/pdf/1707.06347v2
|
We propose a new family of policy gradient methods for reinforcement
learning, which alternate between sampling data through interaction with the
environment, and optimizing a "surrogate" objective function using stochastic
gradient ascent. Whereas standard policy gradient methods perform one gradient
update per data sample, we propose a novel objective function that enables
multiple epochs of minibatch updates. The new methods, which we call proximal
policy optimization (PPO), have some of the benefits of trust region policy
optimization (TRPO), but they are much simpler to implement, more general, and
have better sample complexity (empirically). Our experiments test PPO on a
collection of benchmark tasks, including simulated robotic locomotion and Atari
game playing, and we show that PPO outperforms other online policy gradient
methods, and overall strikes a favorable balance between sample complexity,
simplicity, and wall-time.
|
Benchmarking Recommendation, Classification, and Tracing Based on
Hugging Face Knowledge Graph
|
2505.17507v1
|
DEKR
|
\cite{DEKR}
|
{DEKR:} Description Enhanced Knowledge Graph for Machine Learning
Method Recommendation
| null | null | true | false |
Xianshuai Cao and
Yuliang Shi and
Han Yu and
Jihu Wang and
Xinjun Wang and
Zhongmin Yan and
Zhiyong Chen
| 2,021 | null |
https://doi.org/10.1145/3404835.3462900
|
10.1145/3404835.3462900
| null |
{DEKR:} Description Enhanced Knowledge Graph for Machine Learning
Method Recommendation
|
Description Enhanced Knowledge Graph for Machine Learning ...
|
https://www.researchgate.net/publication/353188658_DEKR_Description_Enhanced_Knowledge_Graph_for_Machine_Learning_Method_Recommendation
|
To further improve the performance of machine learning method recommendation, cross-modal knowledge graph contrastive learning (Cao et al., 2022) maximized the
|
Benchmarking Recommendation, Classification, and Tracing Based on
Hugging Face Knowledge Graph
|
2505.17507v1
|
tse23
|
\cite{tse23}
|
Task-Oriented {ML/DL} Library Recommendation Based on a Knowledge
Graph
| null | null | true | false |
Mingwei Liu and
Chengyuan Zhao and
Xin Peng and
Simin Yu and
Haofen Wang and
Chaofeng Sha
| 2,023 | null |
https://doi.org/10.1109/TSE.2023.3285280
|
10.1109/TSE.2023.3285280
|
{IEEE} Trans. Software Eng.
|
Task-Oriented {ML/DL} Library Recommendation Based on a Knowledge
Graph
|
Task-Oriented ML/DL Library Recommendation Based on ...
|
https://www.researchgate.net/publication/371549606_Task-Oriented_MLDL_Library_Recommendation_based_on_a_Knowledge_Graph
|
AI applications often use ML/DL (Machine Learning/Deep Learning) models to implement specific AI tasks. As application developers usually are not AI experts, they often choose to integrate existing implementations of ML/DL models as libraries for their AI tasks. It constructs a knowledge graph that captures AI tasks, ML/DL models, model implementations, repositories, and their relationships by extracting knowledge from different sources such as ML/DL resource websites, papers, ML/DL frameworks, and repositories. Based on the knowledge graph, MLTaskKG recommends ML/DL libraries for developers by matching their requirements on tasks, model characteristics, and implementation information. Abstract—AI applications often use ML/DL(Machine Learning/Deep Learning)models to implement specific AI tasks.As application a knowledge graph that captures AI tasks,ML/DL models,model implementations,repositories,and their relationships b y extracting
|
Benchmarking Recommendation, Classification, and Tracing Based on
Hugging Face Knowledge Graph
|
2505.17507v1
|
OAGBench
|
\cite{OAGBench}
|
OAG-Bench: {A} Human-Curated Benchmark for Academic Graph Mining
| null | null | true | false |
Fanjin Zhang and
Shijie Shi and
Yifan Zhu and
Bo Chen and
Yukuo Cen and
Jifan Yu and
Yelin Chen and
Lulu Wang and
Qingfei Zhao and
Yuqing Cheng and
Tianyi Han and
Yuwei An and
Dan Zhang and
Weng Lam Tam and
Kun Cao and
Yunhe Pang and
Xinyu Guan and
Huihui Yuan and
Jian Song and
Xiaoyan Li and
Yuxiao Dong and
Jie Tang
| 2,024 | null |
https://doi.org/10.1145/3637528.3672354
|
10.1145/3637528.3672354
| null |
OAG-Bench: {A} Human-Curated Benchmark for Academic Graph Mining
|
[PDF] A Human-Curated Benchmark for Academic Graph Mining - arXiv
|
https://arxiv.org/pdf/2402.15810
|
OAG-Bench is a comprehensive, human-curated benchmark for academic graph mining, based on the Open Academic Graph, covering 10 tasks, 20 datasets, and 70+
|
Benchmarking Recommendation, Classification, and Tracing Based on
Hugging Face Knowledge Graph
|
2505.17507v1
|
paper2repo
|
\cite{paper2repo}
|
paper2repo: GitHub Repository Recommendation for Academic Papers
|
http://arxiv.org/abs/2004.06059v1
|
GitHub has become a popular social application platform, where a large number
of users post their open source projects. In particular, an increasing number
of researchers release repositories of source code related to their research
papers in order to attract more people to follow their work. Motivated by this
trend, we describe a novel item-item cross-platform recommender system,
$\textit{paper2repo}$, that recommends relevant repositories on GitHub that
match a given paper in an academic search system such as Microsoft Academic.
The key challenge is to identify the similarity between an input paper and its
related repositories across the two platforms, $\textit{without the benefit of
human labeling}$. Towards that end, paper2repo integrates text encoding and
constrained graph convolutional networks (GCN) to automatically learn and map
the embeddings of papers and repositories into the same space, where proximity
offers the basis for recommendation. To make our method more practical in real
life systems, labels used for model training are computed automatically from
features of user actions on GitHub. In machine learning, such automatic
labeling is often called {\em distant supervision\/}. To the authors'
knowledge, this is the first distant-supervised cross-platform (paper to
repository) matching system. We evaluate the performance of paper2repo on
real-world data sets collected from GitHub and Microsoft Academic. Results
demonstrate that it outperforms other state of the art recommendation methods.
| true | true |
Huajie Shao and
Dachun Sun and
Jiahao Wu and
Zecheng Zhang and
Aston Zhang and
Shuochao Yao and
Shengzhong Liu and
Tianshi Wang and
Chao Zhang and
Tarek F. Abdelzaher
| 2,020 | null |
https://doi.org/10.1145/3366423.3380145
|
10.1145/3366423.3380145
| null |
paper2repo: GitHub Repository Recommendation for Academic Papers
|
paper2repo: GitHub Repository Recommendation for Academic Papers
|
http://arxiv.org/pdf/2004.06059v1
|
GitHub has become a popular social application platform, where a large number
of users post their open source projects. In particular, an increasing number
of researchers release repositories of source code related to their research
papers in order to attract more people to follow their work. Motivated by this
trend, we describe a novel item-item cross-platform recommender system,
$\textit{paper2repo}$, that recommends relevant repositories on GitHub that
match a given paper in an academic search system such as Microsoft Academic.
The key challenge is to identify the similarity between an input paper and its
related repositories across the two platforms, $\textit{without the benefit of
human labeling}$. Towards that end, paper2repo integrates text encoding and
constrained graph convolutional networks (GCN) to automatically learn and map
the embeddings of papers and repositories into the same space, where proximity
offers the basis for recommendation. To make our method more practical in real
life systems, labels used for model training are computed automatically from
features of user actions on GitHub. In machine learning, such automatic
labeling is often called {\em distant supervision\/}. To the authors'
knowledge, this is the first distant-supervised cross-platform (paper to
repository) matching system. We evaluate the performance of paper2repo on
real-world data sets collected from GitHub and Microsoft Academic. Results
demonstrate that it outperforms other state of the art recommendation methods.
|
Benchmarking Recommendation, Classification, and Tracing Based on
Hugging Face Knowledge Graph
|
2505.17507v1
|
RepoRecommendation
|
\cite{RepoRecommendation}
|
Personalized Repository Recommendation Service for Developers with
Multi-modal Features Learning
| null | null | true | false |
Yueshen Xu and
Yuhong Jiang and
Xinkui Zhao and
Ying Li and
Rui Li
| 2,023 | null |
https://doi.org/10.1109/ICWS60048.2023.00064
|
10.1109/ICWS60048.2023.00064
| null |
Personalized Repository Recommendation Service for Developers with
Multi-modal Features Learning
|
AIDC-AI/Awesome-Unified-Multimodal-Models
|
https://github.com/AIDC-AI/Awesome-Unified-Multimodal-Models
|
| ANOLE | ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text GenerationImage 11: GitHub Repo stars | arXiv | 2024/07/08 | Github | - | | MM-Interleaved | MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature SynchronizerImage 20: GitHub Repo stars | arXiv | 2024/01/18 | Github | - | | Nexus-Gen | Nexus-Gen: A Unified Model for Image Understanding, Generation, and EditingImage 27: GitHub Repo stars | arXiv | 2025/04/30 | Github | Demo | | VARGPT | VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language ModelImage 38: GitHub Repo stars | arXiv | 2025/01/21 | Github | - |
|
Benchmarking Recommendation, Classification, and Tracing Based on
Hugging Face Knowledge Graph
|
2505.17507v1
|
GRETA
|
\cite{GRETA}
|
{GRETA:} Graph-Based Tag Assignment for GitHub Repositories
| null | null | true | false |
Xuyang Cai and
Jiangang Zhu and
Beijun Shen and
Yuting Chen
| 2,016 | null |
https://doi.org/10.1109/COMPSAC.2016.124
|
10.1109/COMPSAC.2016.124
| null |
{GRETA:} Graph-Based Tag Assignment for GitHub Repositories
|
GRETA: Graph-Based Tag Assignment for GitHub Repositories
|
https://ieeexplore.ieee.org/iel7/7551592/7551973/07551994.pdf
|
GRETA is a novel, graph-based approach to tag assignment for repositories on GitHub, which allows tags to be assigned by some graph algorithms. GRETA is also a
|
Benchmarking Recommendation, Classification, and Tracing Based on
Hugging Face Knowledge Graph
|
2505.17507v1
|
EASE24
|
\cite{EASE24}
|
Automated categorization of pre-trained models for software engineering:
A case study with a Hugging Face dataset
|
http://arxiv.org/abs/2405.13185v1
|
Software engineering (SE) activities have been revolutionized by the advent
of pre-trained models (PTMs), defined as large machine learning (ML) models
that can be fine-tuned to perform specific SE tasks. However, users with
limited expertise may need help to select the appropriate model for their
current task. To tackle the issue, the Hugging Face (HF) platform simplifies
the use of PTMs by collecting, storing, and curating several models.
Nevertheless, the platform currently lacks a comprehensive categorization of
PTMs designed specifically for SE, i.e., the existing tags are more suited to
generic ML categories.
This paper introduces an approach to address this gap by enabling the
automatic classification of PTMs for SE tasks. First, we utilize a public dump
of HF to extract PTMs information, including model documentation and associated
tags. Then, we employ a semi-automated method to identify SE tasks and their
corresponding PTMs from existing literature. The approach involves creating an
initial mapping between HF tags and specific SE tasks, using a similarity-based
strategy to identify PTMs with relevant tags. The evaluation shows that model
cards are informative enough to classify PTMs considering the pipeline tag.
Moreover, we provide a mapping between SE tasks and stored PTMs by relying on
model names.
| true | true |
Claudio Di Sipio and
Riccardo Rubei and
Juri Di Rocco and
Davide Di Ruscio and
Phuong T. Nguyen
| 2,024 | null |
https://doi.org/10.1145/3661167.3661215
|
10.1145/3661167.3661215
| null |
Automated categorization of pre-trained models for software engineering:
A case study with a Hugging Face dataset
|
Automated categorization of pre-trained models for software ... - arXiv
|
https://arxiv.org/abs/2405.13185
|
To tackle the issue, the Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. Nevertheless
|
Benchmarking Recommendation, Classification, and Tracing Based on
Hugging Face Knowledge Graph
|
2505.17507v1
|
ESEM24
|
\cite{ESEM24}
|
Automatic Categorization of GitHub Actions with Transformers and
Few-shot Learning
|
http://arxiv.org/abs/2407.16946v1
|
In the GitHub ecosystem, workflows are used as an effective means to automate
development tasks and to set up a Continuous Integration and Delivery (CI/CD
pipeline). GitHub Actions (GHA) have been conceived to provide developers with
a practical tool to create and maintain workflows, avoiding reinventing the
wheel and cluttering the workflow with shell commands. Properly leveraging the
power of GitHub Actions can facilitate the development processes, enhance
collaboration, and significantly impact project outcomes. To expose actions to
search engines, GitHub allows developers to assign them to one or more
categories manually. These are used as an effective means to group actions
sharing similar functionality. Nevertheless, while providing a practical way to
execute workflows, many actions have unclear purposes, and sometimes they are
not categorized. In this work, we bridge such a gap by conceptualizing Gavel, a
practical solution to increasing the visibility of actions in GitHub. By
leveraging the content of README.MD files for each action, we use
Transformer--a deep learning algorithm--to assign suitable categories to the
action. We conducted an empirical investigation and compared Gavel with a
state-of-the-art baseline. The experimental results show that our proposed
approach can assign categories to GitHub actions effectively, thus
outperforming the state-of-the-art baseline.
| true | true |
Phuong T. Nguyen and
Juri Di Rocco and
Claudio Di Sipio and
Mudita Shakya and
Davide Di Ruscio and
Massimiliano Di Penta
| 2,024 | null |
https://doi.org/10.1145/3674805.3690752
|
10.1145/3674805.3690752
| null |
Automatic Categorization of GitHub Actions with Transformers and
Few-shot Learning
|
Automatic Categorization of GitHub Actions with Transformers and ...
|
https://arxiv.org/html/2407.16946v1
|
a GitHub actions visibility elevator based on transformers and few-shot learning to make actions more visible and accessible to developers.
|
Benchmarking Recommendation, Classification, and Tracing Based on
Hugging Face Knowledge Graph
|
2505.17507v1
|
issue-PR-link-prediction
|
\cite{issue-PR-link-prediction}
|
Improving Issue-PR Link Prediction via Knowledge-Aware Heterogeneous
Graph Learning
| null | null | true | false |
Shuotong Bai and
Huaxiao Liu and
Enyan Dai and
Lei Liu
| 2,024 | null |
https://doi.org/10.1109/TSE.2024.3408448
|
10.1109/TSE.2024.3408448
|
{IEEE} Trans. Software Eng.
|
Improving Issue-PR Link Prediction via Knowledge-Aware Heterogeneous
Graph Learning
|
Improving Issue-PR Link Prediction via Knowledge-Aware ...
|
https://www.researchgate.net/publication/381145630_Improving_Issue-PR_Link_Prediction_via_Knowledge-aware_Heterogeneous_Graph_Learning
|
This method combines vector similarity, clustering techniques, and a deep learning model to improve the recommendation process. Additionally, Bai et al. [11]
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
kharitonov2019federated
|
\cite{kharitonov2019federated}
|
Federated online learning to rank with evolution strategies
| null | null | true | false |
Kharitonov, Eugene
| 2,019 | null | null | null | null |
Federated online learning to rank with evolution strategies
|
Federated Online Learning to Rank with Evolution Strategies
|
https://arvinzhuang.github.io/publication/ECIR2021FOLTR
|
Online Learning to Rank (OLTR) optimizes ranking models using implicit users' feedback, such as clicks, directly manipulating search engine results in
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
wang2021federated
|
\cite{wang2021federated}
|
Federated online learning to rank with evolution strategies: a reproducibility study
| null | null | true | false |
Wang, Shuyi and Zhuang, Shengyao and Zuccon, Guido
| 2,021 | null | null | null | null |
Federated online learning to rank with evolution strategies: a reproducibility study
|
Federated Online Learning to Rank with Evolution Strategies
|
https://arvinzhuang.github.io/publication/ECIR2021FOLTR
|
Abstract. Online Learning to Rank (OLTR) optimizes ranking models using implicit users' feedback, such as clicks, directly manipulating search engine results in
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
wang2021effective
|
\cite{wang2021effective}
|
Effective and privacy-preserving federated online learning to rank
| null | null | true | false |
Wang, Shuyi and Liu, Bing and Zhuang, Shengyao and Zuccon, Guido
| 2,021 | null | null | null | null |
Effective and privacy-preserving federated online learning to rank
|
Effective and Privacy-preserving Federated Online Learning to Rank
|
https://dl.acm.org/doi/10.1145/3471158.3472236
|
Empirical evaluation shows FPDGD significantly outperforms the only other federated OLTR method. In addition, FPDGD is more robust across different privacy
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
oosterhuis2018differentiable
|
\cite{oosterhuis2018differentiable}
|
Differentiable Unbiased Online Learning to Rank
|
http://arxiv.org/abs/1809.08415v1
|
Online Learning to Rank (OLTR) methods optimize rankers based on user
interactions. State-of-the-art OLTR methods are built specifically for linear
models. Their approaches do not extend well to non-linear models such as neural
networks. We introduce an entirely novel approach to OLTR that constructs a
weighted differentiable pairwise loss after each interaction: Pairwise
Differentiable Gradient Descent (PDGD). PDGD breaks away from the traditional
approach that relies on interleaving or multileaving and extensive sampling of
models to estimate gradients. Instead, its gradient is based on inferring
preferences between document pairs from user clicks and can optimize any
differentiable model. We prove that the gradient of PDGD is unbiased w.r.t.
user document pair preferences. Our experiments on the largest publicly
available Learning to Rank (LTR) datasets show considerable and significant
improvements under all levels of interaction noise. PDGD outperforms existing
OLTR methods both in terms of learning speed as well as final convergence.
Furthermore, unlike previous OLTR methods, PDGD also allows for non-linear
models to be optimized effectively. Our results show that using a neural
network leads to even better performance at convergence than a linear model. In
summary, PDGD is an efficient and unbiased OLTR approach that provides a better
user experience than previously possible.
| true | true |
Oosterhuis, Harrie and de Rijke, Maarten
| 2,018 | null | null | null | null |
Differentiable Unbiased Online Learning to Rank
|
Differentiable Unbiased Online Learning to Rank
|
http://arxiv.org/pdf/1809.08415v1
|
Online Learning to Rank (OLTR) methods optimize rankers based on user
interactions. State-of-the-art OLTR methods are built specifically for linear
models. Their approaches do not extend well to non-linear models such as neural
networks. We introduce an entirely novel approach to OLTR that constructs a
weighted differentiable pairwise loss after each interaction: Pairwise
Differentiable Gradient Descent (PDGD). PDGD breaks away from the traditional
approach that relies on interleaving or multileaving and extensive sampling of
models to estimate gradients. Instead, its gradient is based on inferring
preferences between document pairs from user clicks and can optimize any
differentiable model. We prove that the gradient of PDGD is unbiased w.r.t.
user document pair preferences. Our experiments on the largest publicly
available Learning to Rank (LTR) datasets show considerable and significant
improvements under all levels of interaction noise. PDGD outperforms existing
OLTR methods both in terms of learning speed as well as final convergence.
Furthermore, unlike previous OLTR methods, PDGD also allows for non-linear
models to be optimized effectively. Our results show that using a neural
network leads to even better performance at convergence than a linear model. In
summary, PDGD is an efficient and unbiased OLTR approach that provides a better
user experience than previously possible.
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
wang2022non
|
\cite{wang2022non}
|
Is Non-IID Data a Threat in Federated Online Learning to Rank?
|
http://arxiv.org/abs/2204.09272v2
|
In this perspective paper we study the effect of non independent and
identically distributed (non-IID) data on federated online learning to rank
(FOLTR) and chart directions for future work in this new and largely unexplored
research area of Information Retrieval. In the FOLTR process, clients
participate in a federation to jointly create an effective ranker from the
implicit click signal originating in each client, without the need to share
data (documents, queries, clicks). A well-known factor that affects the
performance of federated learning systems, and that poses serious challenges to
these approaches, is that there may be some type of bias in the way data is
distributed across clients. While FOLTR systems are on their own rights a type
of federated learning system, the presence and effect of non-IID data in FOLTR
has not been studied. To this aim, we first enumerate possible data
distribution settings that may showcase data bias across clients and thus give
rise to the non-IID problem. Then, we study the impact of each setting on the
performance of the current state-of-the-art FOLTR approach, the Federated
Pairwise Differentiable Gradient Descent (FPDGD), and we highlight which data
distributions may pose a problem for FOLTR methods. We also explore how common
approaches proposed in the federated learning literature address non-IID issues
in FOLTR. This allows us to unveil new research gaps that, we argue, future
research in FOLTR should consider. This is an important contribution to the
current state of FOLTR field because, for FOLTR systems to be deployed, the
factors affecting their performance, including the impact of non-IID data, need
to be thoroughly understood.
| true | true |
Wang, Shuyi and Zuccon, Guido
| 2,022 | null | null | null | null |
Is Non-IID Data a Threat in Federated Online Learning to Rank?
|
Is Non-IID Data a Threat in Federated Online Learning to Rank?
|
https://scispace.com/pdf/is-non-iid-data-a-threat-in-federated-online-learning-to-1hxia4ua.pdf
|
ABSTRACT. In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learn- ing to rank
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
wang2023analysis
|
\cite{wang2023analysis}
|
An Analysis of Untargeted Poisoning Attack and Defense Methods for
Federated Online Learning to Rank Systems
|
http://arxiv.org/abs/2307.01565v1
|
Federated online learning to rank (FOLTR) aims to preserve user privacy by
not sharing their searchable data and search interactions, while guaranteeing
high search effectiveness, especially in contexts where individual users have
scarce training data and interactions. For this, FOLTR trains learning to rank
models in an online manner -- i.e. by exploiting users' interactions with the
search systems (queries, clicks), rather than labels -- and federatively --
i.e. by not aggregating interaction data in a central server for training
purposes, but by training instances of a model on each user device on their own
private data, and then sharing the model updates, not the data, across a set of
users that have formed the federation. Existing FOLTR methods build upon
advances in federated learning.
While federated learning methods have been shown effective at training
machine learning models in a distributed way without the need of data sharing,
they can be susceptible to attacks that target either the system's security or
its overall effectiveness.
In this paper, we consider attacks on FOLTR systems that aim to compromise
their search effectiveness. Within this scope, we experiment with and analyse
data and model poisoning attack methods to showcase their impact on FOLTR
search effectiveness. We also explore the effectiveness of defense methods
designed to counteract attacks on FOLTR systems. We contribute an understanding
of the effect of attack and defense methods for FOLTR systems, as well as
identifying the key factors influencing their effectiveness.
| true | true |
Wang, Shuyi and Zuccon, Guido
| 2,023 | null | null | null | null |
An Analysis of Untargeted Poisoning Attack and Defense Methods for
Federated Online Learning to Rank Systems
|
An Analysis of Untargeted Poisoning Attack and Defense Methods ...
|
https://www.researchgate.net/publication/372136881_An_Analysis_of_Untargeted_Poisoning_Attack_and_Defense_Methods_for_Federated_Online_Learning_to_Rank_Systems
|
Within this scope, we experiment with and analyse data and model poisoning attack methods to showcase their impact on FOLTR search effectiveness. We also
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
jia2022learning
|
\cite{jia2022learning}
|
Learning Neural Ranking Models Online from Implicit User Feedback
|
http://arxiv.org/abs/2201.06658v1
|
Existing online learning to rank (OL2R) solutions are limited to linear
models, which are incompetent to capture possible non-linear relations between
queries and documents. In this work, to unleash the power of representation
learning in OL2R, we propose to directly learn a neural ranking model from
users' implicit feedback (e.g., clicks) collected on the fly. We focus on
RankNet and LambdaRank, due to their great empirical success and wide adoption
in offline settings, and control the notorious explore-exploit trade-off based
on the convergence analysis of neural networks using neural tangent kernel.
Specifically, in each round of result serving, exploration is only performed on
document pairs where the predicted rank order between the two documents is
uncertain; otherwise, the ranker's predicted order will be followed in result
ranking. We prove that under standard assumptions our OL2R solution achieves a
gap-dependent upper regret bound of $O(\log^2(T))$, in which the regret is
defined on the total number of mis-ordered pairs over $T$ rounds. Comparisons
against an extensive set of state-of-the-art OL2R baselines on two public
learning to rank benchmark datasets demonstrate the effectiveness of the
proposed solution.
| true | true |
Jia, Yiling and Wang, Hongning
| 2,022 | null | null | null | null |
Learning Neural Ranking Models Online from Implicit User Feedback
|
Learning Neural Ranking Models Online from Implicit User Feedback
|
http://arxiv.org/pdf/2201.06658v1
|
Existing online learning to rank (OL2R) solutions are limited to linear
models, which are incompetent to capture possible non-linear relations between
queries and documents. In this work, to unleash the power of representation
learning in OL2R, we propose to directly learn a neural ranking model from
users' implicit feedback (e.g., clicks) collected on the fly. We focus on
RankNet and LambdaRank, due to their great empirical success and wide adoption
in offline settings, and control the notorious explore-exploit trade-off based
on the convergence analysis of neural networks using neural tangent kernel.
Specifically, in each round of result serving, exploration is only performed on
document pairs where the predicted rank order between the two documents is
uncertain; otherwise, the ranker's predicted order will be followed in result
ranking. We prove that under standard assumptions our OL2R solution achieves a
gap-dependent upper regret bound of $O(\log^2(T))$, in which the regret is
defined on the total number of mis-ordered pairs over $T$ rounds. Comparisons
against an extensive set of state-of-the-art OL2R baselines on two public
learning to rank benchmark datasets demonstrate the effectiveness of the
proposed solution.
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
wang2018efficient
|
\cite{wang2018efficient}
|
Efficient Exploration of Gradient Space for Online Learning to Rank
|
http://arxiv.org/abs/1805.07317v1
|
Online learning to rank (OL2R) optimizes the utility of returned search
results based on implicit feedback gathered directly from users. To improve the
estimates, OL2R algorithms examine one or more exploratory gradient directions
and update the current ranker if a proposed one is preferred by users via an
interleaved test. In this paper, we accelerate the online learning process by
efficient exploration in the gradient space. Our algorithm, named as Null Space
Gradient Descent, reduces the exploration space to only the \emph{null space}
of recent poorly performing gradients. This prevents the algorithm from
repeatedly exploring directions that have been discouraged by the most recent
interactions with users. To improve sensitivity of the resulting interleaved
test, we selectively construct candidate rankers to maximize the chance that
they can be differentiated by candidate ranking documents in the current query;
and we use historically difficult queries to identify the best ranker when tie
occurs in comparing the rankers. Extensive experimental comparisons with the
state-of-the-art OL2R algorithms on several public benchmarks confirmed the
effectiveness of our proposal algorithm, especially in its fast learning
convergence and promising ranking quality at an early stage.
| true | true |
Wang, Huazheng and Langley, Ramsey and Kim, Sonwoo and McCord-Snook, Eric and Wang, Hongning
| 2,018 | null | null | null | null |
Efficient Exploration of Gradient Space for Online Learning to Rank
|
Efficient Exploration of Gradient Space for Online Learning to Rank
|
http://arxiv.org/pdf/1805.07317v1
|
Online learning to rank (OL2R) optimizes the utility of returned search
results based on implicit feedback gathered directly from users. To improve the
estimates, OL2R algorithms examine one or more exploratory gradient directions
and update the current ranker if a proposed one is preferred by users via an
interleaved test. In this paper, we accelerate the online learning process by
efficient exploration in the gradient space. Our algorithm, named as Null Space
Gradient Descent, reduces the exploration space to only the \emph{null space}
of recent poorly performing gradients. This prevents the algorithm from
repeatedly exploring directions that have been discouraged by the most recent
interactions with users. To improve sensitivity of the resulting interleaved
test, we selectively construct candidate rankers to maximize the chance that
they can be differentiated by candidate ranking documents in the current query;
and we use historically difficult queries to identify the best ranker when tie
occurs in comparing the rankers. Extensive experimental comparisons with the
state-of-the-art OL2R algorithms on several public benchmarks confirmed the
effectiveness of our proposal algorithm, especially in its fast learning
convergence and promising ranking quality at an early stage.
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
liu2021federaser
|
\cite{liu2021federaser}
|
Federaser: Enabling efficient client-level data removal from federated learning models
| null | null | true | false |
Liu, Gaoyang and Ma, Xiaoqiang and Yang, Yang and Wang, Chen and Liu, Jiangchuan
| 2,021 | null | null | null | null |
Federaser: Enabling efficient client-level data removal from federated learning models
|
FedEraser: Enabling Efficient Client-Level Data Removal ...
|
https://www.semanticscholar.org/paper/FedEraser%3A-Enabling-Efficient-Client-Level-Data-Liu-Ma/eadeffdec9fac8fd7f9aea732ca410eb082b7dcf
|
FedEraser is presented, the first federated unlearning method-ology that can eliminate the influence of a federated client's data on the global FL model
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
wu2022federated
|
\cite{wu2022federated}
|
Federated Unlearning with Knowledge Distillation
|
http://arxiv.org/abs/2201.09441v1
|
Federated Learning (FL) is designed to protect the data privacy of each
client during the training process by transmitting only models instead of the
original data. However, the trained model may memorize certain information
about the training data. With the recent legislation on right to be forgotten,
it is crucially essential for the FL model to possess the ability to forget
what it has learned from each client. We propose a novel federated unlearning
method to eliminate a client's contribution by subtracting the accumulated
historical updates from the model and leveraging the knowledge distillation
method to restore the model's performance without using any data from the
clients. This method does not have any restrictions on the type of neural
networks and does not rely on clients' participation, so it is practical and
efficient in the FL system. We further introduce backdoor attacks in the
training process to help evaluate the unlearning effect. Experiments on three
canonical datasets demonstrate the effectiveness and efficiency of our method.
| true | true |
Wu, Chen and Zhu, Sencun and Mitra, Prasenjit
| 2,022 | null | null | null |
arXiv preprint arXiv:2201.09441
|
Federated Unlearning with Knowledge Distillation
|
Federated Unlearning with Knowledge Distillation
|
http://arxiv.org/pdf/2201.09441v1
|
Federated Learning (FL) is designed to protect the data privacy of each
client during the training process by transmitting only models instead of the
original data. However, the trained model may memorize certain information
about the training data. With the recent legislation on right to be forgotten,
it is crucially essential for the FL model to possess the ability to forget
what it has learned from each client. We propose a novel federated unlearning
method to eliminate a client's contribution by subtracting the accumulated
historical updates from the model and leveraging the knowledge distillation
method to restore the model's performance without using any data from the
clients. This method does not have any restrictions on the type of neural
networks and does not rely on clients' participation, so it is practical and
efficient in the FL system. We further introduce backdoor attacks in the
training process to help evaluate the unlearning effect. Experiments on three
canonical datasets demonstrate the effectiveness and efficiency of our method.
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
liu2022right
|
\cite{liu2022right}
|
The Right to be Forgotten in Federated Learning: An Efficient
Realization with Rapid Retraining
|
http://arxiv.org/abs/2203.07320v1
|
In Machine Learning, the emergence of \textit{the right to be forgotten} gave
birth to a paradigm named \textit{machine unlearning}, which enables data
holders to proactively erase their data from a trained model. Existing machine
unlearning techniques focus on centralized training, where access to all
holders' training data is a must for the server to conduct the unlearning
process. It remains largely underexplored about how to achieve unlearning when
full access to all training data becomes unavailable. One noteworthy example is
Federated Learning (FL), where each participating data holder trains locally,
without sharing their training data to the central server. In this paper, we
investigate the problem of machine unlearning in FL systems. We start with a
formal definition of the unlearning problem in FL and propose a rapid
retraining approach to fully erase data samples from a trained FL model. The
resulting design allows data holders to jointly conduct the unlearning process
efficiently while keeping their training data locally. Our formal convergence
and complexity analysis demonstrate that our design can preserve model utility
with high efficiency. Extensive evaluations on four real-world datasets
illustrate the effectiveness and performance of our proposed realization.
| true | true |
Liu, Yi and Xu, Lei and Yuan, Xingliang and Wang, Cong and Li, Bo
| 2,022 | null | null | null | null |
The Right to be Forgotten in Federated Learning: An Efficient
Realization with Rapid Retraining
|
The Right to be Forgotten in Federated Learning: An Efficient ...
|
https://ieeexplore.ieee.org/iel7/9796607/9796652/09796721.pdf
|
This paper proposes a rapid retraining approach in Federated Learning to erase data samples, using a distributed Newton-type model update algorithm.
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
halimi2022federated
|
\cite{halimi2022federated}
|
Federated Unlearning: How to Efficiently Erase a Client in FL?
|
http://arxiv.org/abs/2207.05521v3
|
With privacy legislation empowering the users with the right to be forgotten,
it has become essential to make a model amenable for forgetting some of its
training data. However, existing unlearning methods in the machine learning
context can not be directly applied in the context of distributed settings like
federated learning due to the differences in learning protocol and the presence
of multiple actors. In this paper, we tackle the problem of federated
unlearning for the case of erasing a client by removing the influence of their
entire local data from the trained global model. To erase a client, we propose
to first perform local unlearning at the client to be erased, and then use the
locally unlearned model as the initialization to run very few rounds of
federated learning between the server and the remaining clients to obtain the
unlearned global model. We empirically evaluate our unlearning method by
employing multiple performance measures on three datasets, and demonstrate that
our unlearning method achieves comparable performance as the gold standard
unlearning method of federated retraining from scratch, while being
significantly efficient. Unlike prior works, our unlearning method neither
requires global access to the data used for training nor the history of the
parameter updates to be stored by the server or any of the clients.
| true | true |
Halimi, Anisa and Kadhe, Swanand and Rawat, Ambrish and Baracaldo, Nathalie
| 2,022 | null | null | null |
arXiv preprint arXiv:2207.05521
|
Federated Unlearning: How to Efficiently Erase a Client in FL?
|
Federated Unlearning: How to Efficiently Erase a Client in FL?
|
http://arxiv.org/pdf/2207.05521v3
|
With privacy legislation empowering the users with the right to be forgotten,
it has become essential to make a model amenable for forgetting some of its
training data. However, existing unlearning methods in the machine learning
context can not be directly applied in the context of distributed settings like
federated learning due to the differences in learning protocol and the presence
of multiple actors. In this paper, we tackle the problem of federated
unlearning for the case of erasing a client by removing the influence of their
entire local data from the trained global model. To erase a client, we propose
to first perform local unlearning at the client to be erased, and then use the
locally unlearned model as the initialization to run very few rounds of
federated learning between the server and the remaining clients to obtain the
unlearned global model. We empirically evaluate our unlearning method by
employing multiple performance measures on three datasets, and demonstrate that
our unlearning method achieves comparable performance as the gold standard
unlearning method of federated retraining from scratch, while being
significantly efficient. Unlike prior works, our unlearning method neither
requires global access to the data used for training nor the history of the
parameter updates to be stored by the server or any of the clients.
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
yuan2023federated
|
\cite{yuan2023federated}
|
Federated Unlearning for On-Device Recommendation
|
http://arxiv.org/abs/2210.10958v2
|
The increasing data privacy concerns in recommendation systems have made
federated recommendations (FedRecs) attract more and more attention. Existing
FedRecs mainly focus on how to effectively and securely learn personal
interests and preferences from their on-device interaction data. Still, none of
them considers how to efficiently erase a user's contribution to the federated
training process. We argue that such a dual setting is necessary. First, from
the privacy protection perspective, ``the right to be forgotten'' requires that
users have the right to withdraw their data contributions. Without the
reversible ability, FedRecs risk breaking data protection regulations. On the
other hand, enabling a FedRec to forget specific users can improve its
robustness and resistance to malicious clients' attacks. To support user
unlearning in FedRecs, we propose an efficient unlearning method FRU (Federated
Recommendation Unlearning), inspired by the log-based rollback mechanism of
transactions in database management systems. It removes a user's contribution
by rolling back and calibrating the historical parameter updates and then uses
these updates to speed up federated recommender reconstruction. However,
storing all historical parameter updates on resource-constrained personal
devices is challenging and even infeasible. In light of this challenge, we
propose a small-sized negative sampling method to reduce the number of item
embedding updates and an importance-based update selection mechanism to store
only important model updates. To evaluate the effectiveness of FRU, we propose
an attack method to disturb FedRecs via a group of compromised users and use
FRU to recover recommenders by eliminating these users' influence. Finally, we
conduct experiments on two real-world recommendation datasets with two widely
used FedRecs to show the efficiency and effectiveness of our proposed
approaches.
| true | true |
Yuan, Wei and Yin, Hongzhi and Wu, Fangzhao and Zhang, Shijie and He, Tieke and Wang, Hao
| 2,023 | null | null | null | null |
Federated Unlearning for On-Device Recommendation
|
Federated Unlearning for On-Device Recommendation
|
https://dl.acm.org/doi/10.1145/3539597.3570463
|
To support user unlearning in federated recommendation systems, we propose an efficient unlearning method FRU (Federated Recommendation Unlearning), inspired by
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
zhu2023heterogeneous
|
\cite{zhu2023heterogeneous}
|
Heterogeneous Federated Knowledge Graph Embedding Learning and
Unlearning
|
http://arxiv.org/abs/2302.02069v2
|
Federated Learning (FL) recently emerges as a paradigm to train a global
machine learning model across distributed clients without sharing raw data.
Knowledge Graph (KG) embedding represents KGs in a continuous vector space,
serving as the backbone of many knowledge-driven applications. As a promising
combination, federated KG embedding can fully take advantage of knowledge
learned from different clients while preserving the privacy of local data.
However, realistic problems such as data heterogeneity and knowledge forgetting
still remain to be concerned. In this paper, we propose FedLU, a novel FL
framework for heterogeneous KG embedding learning and unlearning. To cope with
the drift between local optimization and global convergence caused by data
heterogeneity, we propose mutual knowledge distillation to transfer local
knowledge to global, and absorb global knowledge back. Moreover, we present an
unlearning method based on cognitive neuroscience, which combines retroactive
interference and passive decay to erase specific knowledge from local clients
and propagate to the global model by reusing knowledge distillation. We
construct new datasets for assessing realistic performance of the
state-of-the-arts. Extensive experiments show that FedLU achieves superior
results in both link prediction and knowledge forgetting.
| true | true |
Zhu, Xiangrong and Li, Guangyao and Hu, Wei
| 2,023 | null | null | null | null |
Heterogeneous Federated Knowledge Graph Embedding Learning and
Unlearning
|
Heterogeneous Federated Knowledge Graph Embedding ...
|
https://dl.acm.org/doi/10.1145/3543507.3583305
|
In this paper, we propose FedLU, a novel FL framework for heterogeneous KG embedding learning and unlearning. To cope with the drift between
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
wang2024forget
|
\cite{wang2024forget}
|
How to Forget Clients in Federated Online Learning to Rank?
|
http://arxiv.org/abs/2401.13410v1
|
Data protection legislation like the European Union's General Data Protection
Regulation (GDPR) establishes the \textit{right to be forgotten}: a user
(client) can request contributions made using their data to be removed from
learned models. In this paper, we study how to remove the contributions made by
a client participating in a Federated Online Learning to Rank (FOLTR) system.
In a FOLTR system, a ranker is learned by aggregating local updates to the
global ranking model. Local updates are learned in an online manner at a
client-level using queries and implicit interactions that have occurred within
that specific client. By doing so, each client's local data is not shared with
other clients or with a centralised search service, while at the same time
clients can benefit from an effective global ranking model learned from
contributions of each client in the federation.
In this paper, we study an effective and efficient unlearning method that can
remove a client's contribution without compromising the overall ranker
effectiveness and without needing to retrain the global ranker from scratch. A
key challenge is how to measure whether the model has unlearned the
contributions from the client $c^*$ that has requested removal. For this, we
instruct $c^*$ to perform a poisoning attack (add noise to this client updates)
and then we measure whether the impact of the attack is lessened when the
unlearning process has taken place. Through experiments on four datasets, we
demonstrate the effectiveness and efficiency of the unlearning strategy under
different combinations of parameter settings.
| true | true |
Wang, Shuyi and Liu, Bing and Zuccon, Guido
| 2,024 | null | null | null | null |
How to Forget Clients in Federated Online Learning to Rank?
|
How to Forget Clients in Federated Online Learning to Rank?
|
http://arxiv.org/pdf/2401.13410v1
|
Data protection legislation like the European Union's General Data Protection
Regulation (GDPR) establishes the \textit{right to be forgotten}: a user
(client) can request contributions made using their data to be removed from
learned models. In this paper, we study how to remove the contributions made by
a client participating in a Federated Online Learning to Rank (FOLTR) system.
In a FOLTR system, a ranker is learned by aggregating local updates to the
global ranking model. Local updates are learned in an online manner at a
client-level using queries and implicit interactions that have occurred within
that specific client. By doing so, each client's local data is not shared with
other clients or with a centralised search service, while at the same time
clients can benefit from an effective global ranking model learned from
contributions of each client in the federation.
In this paper, we study an effective and efficient unlearning method that can
remove a client's contribution without compromising the overall ranker
effectiveness and without needing to retrain the global ranker from scratch. A
key challenge is how to measure whether the model has unlearned the
contributions from the client $c^*$ that has requested removal. For this, we
instruct $c^*$ to perform a poisoning attack (add noise to this client updates)
and then we measure whether the impact of the attack is lessened when the
unlearning process has taken place. Through experiments on four datasets, we
demonstrate the effectiveness and efficiency of the unlearning strategy under
different combinations of parameter settings.
|
Unlearning for Federated Online Learning to Rank: A Reproducibility
Study
|
2505.12791v1
|
shejwalkar2021manipulating
|
\cite{shejwalkar2021manipulating}
|
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and
Defenses for Federated Learning
| null | null | true | false |
Shejwalkar, Virat and Houmansadr, Amir
| 2,021 | null | null | null | null |
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and
Defenses for Federated Learning
|
Optimizing Model Poisoning Attacks and Defenses for Federat...
|
https://www.youtube.com/watch?v=G2VYRnLqAXE
|
SESSION 6C-3 Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning Federated learning (FL)
|
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval
Knowledge Acquisition
|
2505.07166v1
|
karpukhin2020dense
|
\cite{karpukhin2020dense}
|
Dense Passage Retrieval for Open-Domain Question Answering
|
http://arxiv.org/abs/2004.04906v3
|
Open-domain question answering relies on efficient passage retrieval to
select candidate contexts, where traditional sparse vector space models, such
as TF-IDF or BM25, are the de facto method. In this work, we show that
retrieval can be practically implemented using dense representations alone,
where embeddings are learned from a small number of questions and passages by a
simple dual-encoder framework. When evaluated on a wide range of open-domain QA
datasets, our dense retriever outperforms a strong Lucene-BM25 system largely
by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our
end-to-end QA system establish new state-of-the-art on multiple open-domain QA
benchmarks.
| true | true |
Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau
| 2,020 | null | null | null | null |
Dense Passage Retrieval for Open-Domain Question Answering
|
[2004.04906] Dense Passage Retrieval for Open-Domain ...
|
https://arxiv.org/abs/2004.04906
|
**arXiv:2004.04906** (cs) Authors:Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih View a PDF of the paper titled Dense Passage Retrieval for Open-Domain Question Answering, by Vladimir Karpukhin and 7 other authors View a PDF of the paper titled Dense Passage Retrieval for Open-Domain Question Answering, by Vladimir Karpukhin and 7 other authors - [x] Bibliographic Explorer Toggle - [x] Connected Papers Toggle - [x] Litmaps Toggle - [x] alphaXiv Toggle - [x] Links to Code Toggle - [x] DagsHub Toggle - [x] GotitPub Toggle - [x] Links to Code Toggle - [x] ScienceCast Toggle - [x] Replicate Toggle - [x] Spaces Toggle - [x] Spaces Toggle - [x] Core recommender toggle
|
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval
Knowledge Acquisition
|
2505.07166v1
|
izacard2021contriever
|
\cite{izacard2021contriever}
|
Contriever: A Fully Unsupervised Dense Retriever
| null | null | true | false |
Izacard, Gautier and Grave, Edouard
| 2,021 | null | null | null | null |
Contriever: A Fully Unsupervised Dense Retriever
|
Unsupervised Dense Information Retrieval with Contrastive Learning
|
https://fanpu.io/summaries/2024-10-07-unsupervised-dense-information-retrieval-with-contrastive-learning/
|
Contriever is one of the most competitive & popular baselines for retrievers, and shows how unsupervised techniques have broad appeal. Not
|
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval
Knowledge Acquisition
|
2505.07166v1
|
reimers2019sentence
|
\cite{reimers2019sentence}
|
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
|
http://arxiv.org/abs/1908.10084v1
|
BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new
state-of-the-art performance on sentence-pair regression tasks like semantic
textual similarity (STS). However, it requires that both sentences are fed into
the network, which causes a massive computational overhead: Finding the most
similar pair in a collection of 10,000 sentences requires about 50 million
inference computations (~65 hours) with BERT. The construction of BERT makes it
unsuitable for semantic similarity search as well as for unsupervised tasks
like clustering.
In this publication, we present Sentence-BERT (SBERT), a modification of the
pretrained BERT network that use siamese and triplet network structures to
derive semantically meaningful sentence embeddings that can be compared using
cosine-similarity. This reduces the effort for finding the most similar pair
from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while
maintaining the accuracy from BERT.
We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning
tasks, where it outperforms other state-of-the-art sentence embeddings methods.
| true | true |
Reimers, Nils and Gurevych, Iryna
| 2,019 | null | null | null | null |
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
|
[PDF] Sentence Embeddings using Siamese BERT-Networks
|
https://aclanthology.org/D19-1410.pdf
|
c ⃝2019 Association for Computational Linguistics 3982 Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Nils Reimers and Iryna Gurevych Ubiquitous Knowledge Processing Lab (UKP-TUDA) Department of Computer Science, Technische Universit¨ at Darmstadt www.ukp.tu-darmstadt.de Abstract BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). We fine-tune SBERT on NLI data, which cre-ates sentence embeddings that significantly out-perform other state-of-the-art sentence embedding methods like InferSent (Conneau et al., 2017) and Universal Sentence Encoder (Cer et al., 2018).
|
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval
Knowledge Acquisition
|
2505.07166v1
|
gao2021simcse
|
\cite{gao2021simcse}
|
SimCSE: Simple Contrastive Learning of Sentence Embeddings
|
http://arxiv.org/abs/2104.08821v4
|
This paper presents SimCSE, a simple contrastive learning framework that
greatly advances state-of-the-art sentence embeddings. We first describe an
unsupervised approach, which takes an input sentence and predicts itself in a
contrastive objective, with only standard dropout used as noise. This simple
method works surprisingly well, performing on par with previous supervised
counterparts. We find that dropout acts as minimal data augmentation, and
removing it leads to a representation collapse. Then, we propose a supervised
approach, which incorporates annotated pairs from natural language inference
datasets into our contrastive learning framework by using "entailment" pairs as
positives and "contradiction" pairs as hard negatives. We evaluate SimCSE on
standard semantic textual similarity (STS) tasks, and our unsupervised and
supervised models using BERT base achieve an average of 76.3% and 81.6%
Spearman's correlation respectively, a 4.2% and 2.2% improvement compared to
the previous best results. We also show -- both theoretically and empirically
-- that the contrastive learning objective regularizes pre-trained embeddings'
anisotropic space to be more uniform, and it better aligns positive pairs when
supervised signals are available.
| true | true |
Gao, Tianyu and Yao, Xing and Chen, Dan
| 2,021 | null | null | null | null |
SimCSE: Simple Contrastive Learning of Sentence Embeddings
|
SimCSE: Simple Contrastive Learning of Sentence Embeddings
|
http://arxiv.org/pdf/2104.08821v4
|
This paper presents SimCSE, a simple contrastive learning framework that
greatly advances state-of-the-art sentence embeddings. We first describe an
unsupervised approach, which takes an input sentence and predicts itself in a
contrastive objective, with only standard dropout used as noise. This simple
method works surprisingly well, performing on par with previous supervised
counterparts. We find that dropout acts as minimal data augmentation, and
removing it leads to a representation collapse. Then, we propose a supervised
approach, which incorporates annotated pairs from natural language inference
datasets into our contrastive learning framework by using "entailment" pairs as
positives and "contradiction" pairs as hard negatives. We evaluate SimCSE on
standard semantic textual similarity (STS) tasks, and our unsupervised and
supervised models using BERT base achieve an average of 76.3% and 81.6%
Spearman's correlation respectively, a 4.2% and 2.2% improvement compared to
the previous best results. We also show -- both theoretically and empirically
-- that the contrastive learning objective regularizes pre-trained embeddings'
anisotropic space to be more uniform, and it better aligns positive pairs when
supervised signals are available.
|
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval
Knowledge Acquisition
|
2505.07166v1
|
replama2021
|
\cite{replama2021}
|
RePLAMA: A Decoder-based Dense Retriever for Open-Domain Question Answering
| null | null | true | false |
Smith, John and Doe, Jane
| 2,021 | null | null | null | null |
RePLAMA: A Decoder-based Dense Retriever for Open-Domain Question Answering
|
A Reproducibility Study on Dense Retrieval Knowledge Acquisition
|
https://dl.acm.org/doi/10.1145/3726302.3730332
|
RePLAMA: A Decoder-based Dense Retriever for Open-Domain Question Answering. In Proceedings of the 2021 Conference on Information Retrieval
|
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval
Knowledge Acquisition
|
2505.07166v1
|
promptreps2021
|
\cite{promptreps2021}
|
PromptReps: Enhancing Dense Retrieval with Prompt-based Representations
| null | null | true | false |
Lee, Alex and Kumar, Rahul
| 2,021 | null | null | null | null |
PromptReps: Enhancing Dense Retrieval with Prompt-based Representations
|
[2404.18424] PromptReps: Prompting Large Language Models to ...
|
https://arxiv.org/abs/2404.18424
|
In this paper, we propose PromptReps, which combines the advantages of both categories: no need for training and the ability to retrieve from the whole corpus.
|
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval
Knowledge Acquisition
|
2505.07166v1
|
msmarco
|
\cite{msmarco}
|
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
|
http://arxiv.org/abs/1611.09268v3
|
We introduce a large scale MAchine Reading COmprehension dataset, which we
name MS MARCO. The dataset comprises of 1,010,916 anonymized
questions---sampled from Bing's search query logs---each with a human generated
answer and 182,669 completely human rewritten generated answers. In addition,
the dataset contains 8,841,823 passages---extracted from 3,563,535 web
documents retrieved by Bing---that provide the information necessary for
curating the natural language answers. A question in the MS MARCO dataset may
have multiple answers or no answers at all. Using this dataset, we propose
three different tasks with varying levels of difficulty: (i) predict if a
question is answerable given a set of context passages, and extract and
synthesize the answer as a human would (ii) generate a well-formed answer (if
possible) based on the context passages that can be understood with the
question and passage context, and finally (iii) rank a set of retrieved
passages given a question. The size of the dataset and the fact that the
questions are derived from real user search queries distinguishes MS MARCO from
other well-known publicly available datasets for machine reading comprehension
and question-answering. We believe that the scale and the real-world nature of
this dataset makes it attractive for benchmarking machine reading comprehension
and question-answering models.
| true | true |
Nguyen, Thang and others
| 2,016 | null | null | null | null |
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
|
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
|
http://arxiv.org/pdf/1611.09268v3
|
We introduce a large scale MAchine Reading COmprehension dataset, which we
name MS MARCO. The dataset comprises of 1,010,916 anonymized
questions---sampled from Bing's search query logs---each with a human generated
answer and 182,669 completely human rewritten generated answers. In addition,
the dataset contains 8,841,823 passages---extracted from 3,563,535 web
documents retrieved by Bing---that provide the information necessary for
curating the natural language answers. A question in the MS MARCO dataset may
have multiple answers or no answers at all. Using this dataset, we propose
three different tasks with varying levels of difficulty: (i) predict if a
question is answerable given a set of context passages, and extract and
synthesize the answer as a human would (ii) generate a well-formed answer (if
possible) based on the context passages that can be understood with the
question and passage context, and finally (iii) rank a set of retrieved
passages given a question. The size of the dataset and the fact that the
questions are derived from real user search queries distinguishes MS MARCO from
other well-known publicly available datasets for machine reading comprehension
and question-answering. We believe that the scale and the real-world nature of
this dataset makes it attractive for benchmarking machine reading comprehension
and question-answering models.
|
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval
Knowledge Acquisition
|
2505.07166v1
|
naturalquestions
|
\cite{naturalquestions}
|
Natural Questions: A Benchmark for Question Answering
| null | null | true | false |
Kwiatkowski, Tom and Palomaki, Jenna and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, David and Filatov, Yury and Khashabi, Daniel and Sabharwal, Ashish and others
| 2,019 | null | null | null | null |
Natural Questions: A Benchmark for Question Answering
|
Natural Questions: A Benchmark for Question Answering Research
|
https://scispace.com/papers/natural-questions-a-benchmark-for-question-answering-10mm1ytgmc
|
The Natural Questions corpus, a question answering data set, is presented, introducing robust metrics for the purposes of evaluating question answering systems.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
Frequency23
|
\cite{Frequency23}
|
Frequency Enhanced Hybrid Attention Network for Sequential
Recommendation
|
http://arxiv.org/abs/2304.09184v3
|
The self-attention mechanism, which equips with a strong capability of
modeling long-range dependencies, is one of the extensively used techniques in
the sequential recommendation field. However, many recent studies represent
that current self-attention based models are low-pass filters and are
inadequate to capture high-frequency information. Furthermore, since the items
in the user behaviors are intertwined with each other, these models are
incomplete to distinguish the inherent periodicity obscured in the time domain.
In this work, we shift the perspective to the frequency domain, and propose a
novel Frequency Enhanced Hybrid Attention Network for Sequential
Recommendation, namely FEARec. In this model, we firstly improve the original
time domain self-attention in the frequency domain with a ramp structure to
make both low-frequency and high-frequency information could be explicitly
learned in our approach. Moreover, we additionally design a similar attention
mechanism via auto-correlation in the frequency domain to capture the periodic
characteristics and fuse the time and frequency level attention in a union
model. Finally, both contrastive learning and frequency regularization are
utilized to ensure that multiple views are aligned in both the time domain and
frequency domain. Extensive experiments conducted on four widely used benchmark
datasets demonstrate that the proposed model performs significantly better than
the state-of-the-art approaches.
| true | true |
Du, Xinyu and Yuan, Huanhuan and Zhao, Pengpeng and Qu, Jianfeng and Zhuang, Fuzhen and Liu, Guanfeng and Liu, Yanchi and Sheng, Victor S
| 2,023 | null | null | null | null |
Frequency Enhanced Hybrid Attention Network for Sequential
Recommendation
|
Frequency Enhanced Hybrid Attention Network for ...
|
https://arxiv.org/pdf/2304.09184
|
by X Du · 2023 · Cited by 108 — FEARec is a Frequency Enhanced Hybrid Attention Network for sequential recommendation, improving self-attention in the frequency domain to capture both low and
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
DL4
|
\cite{DL4}
|
Deep learning based recommender system: A survey and new perspectives
| null | null | true | false |
Zhang, Shuai and Yao, Lina and Sun, Aixin and Tay, Yi
| 2,019 | null | null | null |
CSUR
|
Deep learning based recommender system: A survey and new perspectives
|
Deep Learning based Recommender System: A Survey and New Perspectives
|
http://arxiv.org/pdf/1707.07435v7
|
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
Xavier
|
\cite{Xavier}
|
Understanding the difficulty of training deep feedforward neural networks
| null | null | true | false |
Glorot, Xavier and Bengio, Yoshua
| 2,010 | null | null | null | null |
Understanding the difficulty of training deep feedforward neural networks
|
Understanding the difficulty of training deep feedforward ...
|
https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
|
by X Glorot · Cited by 28103 — Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
sse-pt
|
\cite{sse-pt}
|
SSE-PT: Sequential recommendation via personalized transformer
| null | null | true | false |
Wu, Liwei and Li, Shuqing and Hsieh, Cho-Jui and Sharpnack, James
| 2,020 | null | null | null | null |
SSE-PT: Sequential recommendation via personalized transformer
|
SSE-PT: Sequential Recommendation Via Personalized Transformer
|
https://www.researchgate.net/publication/347834874_SSE-PT_Sequential_Recommendation_Via_Personalized_Transformer
|
Sequential recommendation systems process a user's history of interactions into a time-ordered sequence that reflects the evolution of their
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
zhao2023embedding
|
\cite{zhao2023embedding}
|
Embedding in Recommender Systems: A Survey
|
http://arxiv.org/abs/2310.18608v2
|
Recommender systems have become an essential component of many online
platforms, providing personalized recommendations to users. A crucial aspect is
embedding techniques that coverts the high-dimensional discrete features, such
as user and item IDs, into low-dimensional continuous vectors and can enhance
the recommendation performance. Applying embedding techniques captures complex
entity relationships and has spurred substantial research. In this survey, we
provide an overview of the recent literature on embedding techniques in
recommender systems. This survey covers embedding methods like collaborative
filtering, self-supervised learning, and graph-based techniques. Collaborative
filtering generates embeddings capturing user-item preferences, excelling in
sparse data. Self-supervised methods leverage contrastive or generative
learning for various tasks. Graph-based techniques like node2vec exploit
complex relationships in network-rich environments. Addressing the scalability
challenges inherent to embedding methods, our survey delves into innovative
directions within the field of recommendation systems. These directions aim to
enhance performance and reduce computational complexity, paving the way for
improved recommender systems. Among these innovative approaches, we will
introduce Auto Machine Learning (AutoML), hash techniques, and quantization
techniques in this survey. We discuss various architectures and techniques and
highlight the challenges and future directions in these aspects. This survey
aims to provide a comprehensive overview of the state-of-the-art in this
rapidly evolving field and serve as a useful resource for researchers and
practitioners working in the area of recommender systems.
| true | true |
Zhao, Xiangyu and Wang, Maolin and Zhao, Xinjian and Li, Jiansheng and Zhou, Shucheng and Yin, Dawei and Li, Qing and Tang, Jiliang and Guo, Ruocheng
| 2,023 | null | null | null |
arXiv preprint arXiv:2310.18608
|
Embedding in Recommender Systems: A Survey
|
Embedding in Recommender Systems: A Survey
|
http://arxiv.org/pdf/2310.18608v2
|
Recommender systems have become an essential component of many online
platforms, providing personalized recommendations to users. A crucial aspect is
embedding techniques that coverts the high-dimensional discrete features, such
as user and item IDs, into low-dimensional continuous vectors and can enhance
the recommendation performance. Applying embedding techniques captures complex
entity relationships and has spurred substantial research. In this survey, we
provide an overview of the recent literature on embedding techniques in
recommender systems. This survey covers embedding methods like collaborative
filtering, self-supervised learning, and graph-based techniques. Collaborative
filtering generates embeddings capturing user-item preferences, excelling in
sparse data. Self-supervised methods leverage contrastive or generative
learning for various tasks. Graph-based techniques like node2vec exploit
complex relationships in network-rich environments. Addressing the scalability
challenges inherent to embedding methods, our survey delves into innovative
directions within the field of recommendation systems. These directions aim to
enhance performance and reduce computational complexity, paving the way for
improved recommender systems. Among these innovative approaches, we will
introduce Auto Machine Learning (AutoML), hash techniques, and quantization
techniques in this survey. We discuss various architectures and techniques and
highlight the challenges and future directions in these aspects. This survey
aims to provide a comprehensive overview of the state-of-the-art in this
rapidly evolving field and serve as a useful resource for researchers and
practitioners working in the area of recommender systems.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
FMLP
|
\cite{FMLP}
|
Filter-enhanced MLP is All You Need for Sequential Recommendation
|
http://arxiv.org/abs/2202.13556v1
|
Recently, deep neural networks such as RNN, CNN and Transformer have been
applied in the task of sequential recommendation, which aims to capture the
dynamic preference characteristics from logged user behavior data for accurate
recommendation. However, in online platforms, logged user behavior data is
inevitable to contain noise, and deep recommendation models are easy to overfit
on these logged data. To tackle this problem, we borrow the idea of filtering
algorithms from signal processing that attenuates the noise in the frequency
domain. In our empirical experiments, we find that filtering algorithms can
substantially improve representative sequential recommendation models, and
integrating simple filtering algorithms (eg Band-Stop Filter) with an all-MLP
architecture can even outperform competitive Transformer-based models.
Motivated by it, we propose \textbf{FMLP-Rec}, an all-MLP model with learnable
filters for sequential recommendation task. The all-MLP architecture endows our
model with lower time complexity, and the learnable filters can adaptively
attenuate the noise information in the frequency domain. Extensive experiments
conducted on eight real-world datasets demonstrate the superiority of our
proposed method over competitive RNN, CNN, GNN and Transformer-based methods.
Our code and data are publicly available at the link:
\textcolor{blue}{\url{https://github.com/RUCAIBox/FMLP-Rec}}.
| true | true |
Zhou, Kun and Yu, Hui and Zhao, Wayne Xin and Wen, Ji-Rong
| 2,022 | null | null | null | null |
Filter-enhanced MLP is All You Need for Sequential Recommendation
|
Filter-enhanced MLP is All You Need for Sequential Recommendation
|
https://dl.acm.org/doi/10.1145/3485447.3512111
|
We propose FMLP-Rec, an all-MLP model with learnable filters for sequential recommendation task. The all-MLP architecture endows our model with lower time
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
strec
|
\cite{strec}
|
STRec: Sparse Transformer for Sequential Recommendations
| null | null | true | false |
Li, Chengxi and Wang, Yejing and Liu, Qidong and Zhao, Xiangyu and Wang, Wanyu and Wang, Yiqi and Zou, Lixin and Fan, Wenqi and Li, Qing
| 2,023 | null | null | null | null |
STRec: Sparse Transformer for Sequential Recommendations
|
CITE
|
https://aml-cityu.github.io/bibtex/li2023strec.html
|
@inproceedings{li2023strec, title={STRec: Sparse Transformer for Sequential Recommendations}, author={Li, Chengxi and Wang, Yejing and Liu, Qidong and Zhao
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
MLM4Rec
|
\cite{MLM4Rec}
|
Learning Global and Multi-granularity Local Representation with MLP for Sequential Recommendation
| null | null | true | false |
Long, Chao and Yuan, Huanhuan and Fang, Junhua and Xian, Xuefeng and Liu, Guanfeng and Sheng, Victor S and Zhao, Pengpeng
| 2,024 | null | null | null |
ACM Transactions on Knowledge Discovery from Data
|
Learning Global and Multi-granularity Local Representation with MLP for Sequential Recommendation
|
Learning Global and Multi-granularity Local ...
|
https://openreview.net/forum?id=CtsUBneYhu&referrer=%5Bthe%20profile%20of%20Junhua%20Fang%5D(%2Fprofile%3Fid%3D~Junhua_Fang1)
|
Learning Global and Multi-granularity Local Representation with MLP for Sequential Recommendation | OpenReview Learning Global and Multi-granularity Local Representation with MLP for Sequential Recommendation Usually, users’ global and local preferences jointly affect the final recommendation result in different ways. Most existing works use transformers to globally model sequences, which makes them face the dilemma of quadratic computational complexity when dealing with long sequences. To this end, we proposed a parallel architecture for capturing global representation and Multi-granularity Local dependencies with MLP for sequential Recommendation (MLM4Rec). For global representation, we utilize modified MLP-Mixer to capture global information of user sequences due to its simplicity and efficiency. For local representation, we incorporate convolution into MLP and propose a multi-granularity local awareness mechanism for capturing richer local semantic information.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
PEPNet
|
\cite{PEPNet}
|
PEPNet: Parameter and Embedding Personalized Network for Infusing with
Personalized Prior Information
|
http://arxiv.org/abs/2302.01115v3
|
With the increase of content pages and interactive buttons in online services
such as online-shopping and video-watching websites, industrial-scale
recommender systems face challenges in multi-domain and multi-task
recommendations. The core of multi-task and multi-domain recommendation is to
accurately capture user interests in multiple scenarios given multiple user
behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter
and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work
(\textbf{PEPNet})} for multi-domain and multi-task recommendation. PEPNet takes
personalized prior information as input and dynamically scales the bottom-level
Embedding and top-level DNN hidden units through gate mechanisms.
\textit{Embedding Personalized Network (EPNet)} performs personalized selection
on Embedding to fuse features with different importance for different users in
multiple domains. \textit{Parameter Personalized Network (PPNet)} executes
personalized modification on DNN parameters to balance targets with different
sparsity for different users in multiple tasks. We have made a series of
special engineering optimizations combining the Kuaishou training framework and
the online deployment environment. By infusing personalized selection of
Embedding and personalized modification of DNN parameters, PEPNet tailored to
the interests of each individual obtains significant performance gains, with
online improvements exceeding 1\% in multiple task metrics across multiple
domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million
users every day.
| true | true |
Chang, Jianxin and Zhang, Chenbin and Hui, Yiqun and Leng, Dewei and Niu, Yanan and Song, Yang and Gai, Kun
| 2,023 | null | null | null | null |
PEPNet: Parameter and Embedding Personalized Network for Infusing with
Personalized Prior Information
|
[PDF] PEPNet: Parameter and Embedding Personalized Network ... - arXiv
|
https://arxiv.org/pdf/2302.01115
|
Missing: 04/08/2025
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
mb-str
|
\cite{mb-str}
|
Multi-behavior sequential transformer recommender
| null | null | true | false |
Yuan, Enming and Guo, Wei and He, Zhicheng and Guo, Huifeng and Liu, Chengkai and Tang, Ruiming
| 2,022 | null | null | null | null |
Multi-behavior sequential transformer recommender
|
Multi-Behavior Sequential Transformer Recommender
|
https://dl.acm.org/doi/10.1145/3477495.3532023
|
The proposed framework MB-STR, a Multi-Behavior Sequential Transformer Recommender, is equipped with the multi-behavior transformer layer (MB-Trans), the multi
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
lightsan
|
\cite{lightsan}
|
Lighter and better: low-rank decomposed self-attention networks for next-item recommendation
| null | null | true | false |
Fan, Xinyan and Liu, Zheng and Lian, Jianxun and Zhao, Wayne Xin and Xie, Xing and Wen, Ji-Rong
| 2,021 | null | null | null | null |
Lighter and better: low-rank decomposed self-attention networks for next-item recommendation
|
[PDF] Low-Rank Decomposed Self-Attention Networks for Next-Item ...
|
https://www.microsoft.com/en-us/research/wp-content/uploads/2021/05/LighterandBetter_Low-RankDecomposedSelf-AttentionNetworksforNext-ItemRecommendation.pdf
|
Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation Xinyan Fan1,2, Zheng Liu3∗, Jianxun Lian3, Wayne Xin Zhao1,2∗, Xing Xie3, and Ji-Rong Wen1,2 1Gaoling School of Artificial Intelligence, Renmin University of China 2Beijing Key Laboratory of Big Data Management and Analysis Methods 3Microsoft Research Asia {xinyan.fan, jrwen}@ruc.edu.cn, [email protected], {zhengliu, jianxun.lian, xingx}@microsoft.com ABSTRACT Self-attention networks (SANs) have been intensively applied for sequential recommenders, but they are limited due to: (1) the qua-dratic complexity and vulnerability to over-parameterization in self-attention; (2) inaccurate modeling of sequential relations between items due to the implicit position encoding. Our main contributions are summarized as follows: • A novel SANs-based sequential recommender, LightSANs, with two advantages: (1) the low-rank decomposed self-attention for more efficient and precise modeling of context-aware represen-tations; (2) the decoupled position encoding for more effective modeling of sequential relations between items.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
autoseqrec
|
\cite{autoseqrec}
|
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
|
http://arxiv.org/abs/2308.06878v1
|
Sequential recommendation demonstrates the capability to recommend items by
modeling the sequential behavior of users. Traditional methods typically treat
users as sequences of items, overlooking the collaborative relationships among
them. Graph-based methods incorporate collaborative information by utilizing
the user-item interaction graph. However, these methods sometimes face
challenges in terms of time complexity and computational efficiency. To address
these limitations, this paper presents AutoSeqRec, an incremental
recommendation model specifically designed for sequential recommendation tasks.
AutoSeqRec is based on autoencoders and consists of an encoder and three
decoders within the autoencoder architecture. These components consider both
the user-item interaction matrix and the rows and columns of the item
transition matrix. The reconstruction of the user-item interaction matrix
captures user long-term preferences through collaborative filtering. In
addition, the rows and columns of the item transition matrix represent the item
out-degree and in-degree hopping behavior, which allows for modeling the user's
short-term interests. When making incremental recommendations, only the input
matrices need to be updated, without the need to update parameters, which makes
AutoSeqRec very efficient. Comprehensive evaluations demonstrate that
AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing
its robustness and efficiency.
| true | true |
Liu, Sijia and Liu, Jiahao and Gu, Hansu and Li, Dongsheng and Lu, Tun and Zhang, Peng and Gu, Ning
| 2,023 | null | null | null | null |
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
|
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
|
http://arxiv.org/pdf/2308.06878v1
|
Sequential recommendation demonstrates the capability to recommend items by
modeling the sequential behavior of users. Traditional methods typically treat
users as sequences of items, overlooking the collaborative relationships among
them. Graph-based methods incorporate collaborative information by utilizing
the user-item interaction graph. However, these methods sometimes face
challenges in terms of time complexity and computational efficiency. To address
these limitations, this paper presents AutoSeqRec, an incremental
recommendation model specifically designed for sequential recommendation tasks.
AutoSeqRec is based on autoencoders and consists of an encoder and three
decoders within the autoencoder architecture. These components consider both
the user-item interaction matrix and the rows and columns of the item
transition matrix. The reconstruction of the user-item interaction matrix
captures user long-term preferences through collaborative filtering. In
addition, the rows and columns of the item transition matrix represent the item
out-degree and in-degree hopping behavior, which allows for modeling the user's
short-term interests. When making incremental recommendations, only the input
matrices need to be updated, without the need to update parameters, which makes
AutoSeqRec very efficient. Comprehensive evaluations demonstrate that
AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing
its robustness and efficiency.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
HRNN
|
\cite{HRNN}
|
Personalizing Session-based Recommendations with Hierarchical Recurrent
Neural Networks
|
http://arxiv.org/abs/1706.04148v5
|
Session-based recommendations are highly relevant in many modern on-line
services (e.g. e-commerce, video streaming) and recommendation settings.
Recently, Recurrent Neural Networks have been shown to perform very well in
session-based settings. While in many session-based recommendation domains user
identifiers are hard to come by, there are also domains in which user profiles
are readily available. We propose a seamless way to personalize RNN models with
cross-session information transfer and devise a Hierarchical RNN model that
relays end evolves latent hidden states of the RNNs across user sessions.
Results on two industry datasets show large improvements over the session-only
RNNs.
| true | true |
Quadrana, Massimo and Karatzoglou, Alexandros and Hidasi, Bal{\'a}zs and Cremonesi, Paolo
| 2,017 | null | null | null | null |
Personalizing Session-based Recommendations with Hierarchical Recurrent
Neural Networks
|
Personalizing Session-based Recommendations with Hierarchical ...
|
https://www.slideshare.net/slideshow/personalizing-sessionbased-recommendations-with-hierarchical-recurrent-neural-networks/79285884
|
This document summarizes a research paper on personalizing session-based recommendations with hierarchical recurrent neural networks (HRNNs).
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
zhao2023user
|
\cite{zhao2023user}
|
User Retention-oriented Recommendation with Decision Transformer
|
http://arxiv.org/abs/2303.06347v1
|
Improving user retention with reinforcement learning~(RL) has attracted
increasing attention due to its significant importance in boosting user
engagement. However, training the RL policy from scratch without hurting users'
experience is unavoidable due to the requirement of trial-and-error searches.
Furthermore, the offline methods, which aim to optimize the policy without
online interactions, suffer from the notorious stability problem in value
estimation or unbounded variance in counterfactual policy evaluation. To this
end, we propose optimizing user retention with Decision Transformer~(DT), which
avoids the offline difficulty by translating the RL as an autoregressive
problem. However, deploying the DT in recommendation is a non-trivial problem
because of the following challenges: (1) deficiency in modeling the numerical
reward value; (2) data discrepancy between the policy learning and
recommendation generation; (3) unreliable offline performance evaluation. In
this work, we, therefore, contribute a series of strategies for tackling the
exposed issues. We first articulate an efficient reward prompt by weighted
aggregation of meta embeddings for informative reward embedding. Then, we endow
a weighted contrastive learning method to solve the discrepancy between
training and inference. Furthermore, we design two robust offline metrics to
measure user retention. Finally, the significant improvement in the benchmark
datasets demonstrates the superiority of the proposed method.
| true | true |
Zhao, Kesen and Zou, Lixin and Zhao, Xiangyu and Wang, Maolin and Yin, Dawei
| 2,023 | null | null | null | null |
User Retention-oriented Recommendation with Decision Transformer
|
User Retention-oriented Recommendation with Decision ...
|
https://arxiv.org/pdf/2303.06347
|
by K Zhao · 2023 · Cited by 31 — This paper proposes using Decision Transformer (DT) to optimize user retention in recommendation by translating reinforcement learning as an
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
DMAN
|
\cite{DMAN}
|
Dynamic Memory based Attention Network for Sequential Recommendation
|
http://arxiv.org/abs/2102.09269v1
|
Sequential recommendation has become increasingly essential in various online
services. It aims to model the dynamic preferences of users from their
historical interactions and predict their next items. The accumulated user
behavior records on real systems could be very long. This rich data brings
opportunities to track actual interests of users. Prior efforts mainly focus on
making recommendations based on relatively recent behaviors. However, the
overall sequential data may not be effectively utilized, as early interactions
might affect users' current choices. Also, it has become intolerable to scan
the entire behavior sequence when performing inference for each user, since
real-world system requires short response time. To bridge the gap, we propose a
novel long sequential recommendation model, called Dynamic Memory-based
Attention Network (DMAN). It segments the overall long behavior sequence into a
series of sub-sequences, then trains the model and maintains a set of memory
blocks to preserve long-term interests of users. To improve memory fidelity,
DMAN dynamically abstracts each user's long-term interest into its own memory
blocks by minimizing an auxiliary reconstruction loss. Based on the dynamic
memory, the user's short-term and long-term interests can be explicitly
extracted and combined for efficient joint recommendation. Empirical results
over four benchmark datasets demonstrate the superiority of our model in
capturing long-term dependency over various state-of-the-art sequential models.
| true | true |
Tan, Qiaoyu and Zhang, Jianwei and Liu, Ninghao and Huang, Xiao and Yang, Hongxia and Zhou, Jingren and Hu, Xia
| 2,021 | null | null | null | null |
Dynamic Memory based Attention Network for Sequential Recommendation
|
Dynamic Memory based Attention Network for Sequential Recommendation
|
http://arxiv.org/pdf/2102.09269v1
|
Sequential recommendation has become increasingly essential in various online
services. It aims to model the dynamic preferences of users from their
historical interactions and predict their next items. The accumulated user
behavior records on real systems could be very long. This rich data brings
opportunities to track actual interests of users. Prior efforts mainly focus on
making recommendations based on relatively recent behaviors. However, the
overall sequential data may not be effectively utilized, as early interactions
might affect users' current choices. Also, it has become intolerable to scan
the entire behavior sequence when performing inference for each user, since
real-world system requires short response time. To bridge the gap, we propose a
novel long sequential recommendation model, called Dynamic Memory-based
Attention Network (DMAN). It segments the overall long behavior sequence into a
series of sub-sequences, then trains the model and maintains a set of memory
blocks to preserve long-term interests of users. To improve memory fidelity,
DMAN dynamically abstracts each user's long-term interest into its own memory
blocks by minimizing an auxiliary reconstruction loss. Based on the dynamic
memory, the user's short-term and long-term interests can be explicitly
extracted and combined for efficient joint recommendation. Empirical results
over four benchmark datasets demonstrate the superiority of our model in
capturing long-term dependency over various state-of-the-art sequential models.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
koren2009matrix
|
\cite{koren2009matrix}
|
Content-boosted Matrix Factorization Techniques for Recommender Systems
|
http://arxiv.org/abs/1210.5631v2
|
Many businesses are using recommender systems for marketing outreach.
Recommendation algorithms can be either based on content or driven by
collaborative filtering. We study different ways to incorporate content
information directly into the matrix factorization approach of collaborative
filtering. These content-boosted matrix factorization algorithms not only
improve recommendation accuracy, but also provide useful insights about the
contents, as well as make recommendations more easily interpretable.
| true | true |
Koren, Yehuda and Bell, Robert and Volinsky, Chris
| 2,009 | null | null | null |
Computer
|
Content-boosted Matrix Factorization Techniques for Recommender Systems
|
Content-boosted Matrix Factorization Techniques for Recommender ...
|
https://arxiv.org/abs/1210.5631
|
[1210.5631] Content-boosted Matrix Factorization Techniques for Recommender Systems >stat> arXiv:1210.5631 arXiv:1210.5631 (stat) Title:Content-boosted Matrix Factorization Techniques for Recommender Systems View a PDF of the paper titled Content-boosted Matrix Factorization Techniques for Recommender Systems, by Jennifer Nguyen and 1 other authors Cite as:arXiv:1210.5631 [stat.ML] (or arXiv:1210.5631v2 [stat.ML] for this version) View a PDF of the paper titled Content-boosted Matrix Factorization Techniques for Recommender Systems, by Jennifer Nguyen and 1 other authors [x] Bibliographic Explorer Toggle [x] Connected Papers Toggle [x] Litmaps Toggle [x] scite.ai Toggle [x] alphaXiv Toggle [x] Links to Code Toggle [x] DagsHub Toggle [x] GotitPub Toggle [x] Huggingface Toggle [x] Links to Code Toggle [x] ScienceCast Toggle [x] Replicate Toggle [x] Spaces Toggle [x] Spaces Toggle [x] Core recommender toggle
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
Kang01
|
\cite{Kang01}
|
Self-Attentive Sequential Recommendation
|
http://arxiv.org/abs/1808.09781v1
|
Sequential dynamics are a key feature of many modern recommender systems,
which seek to capture the `context' of users' activities on the basis of
actions they have performed recently. To capture such patterns, two approaches
have proliferated: Markov Chains (MCs) and Recurrent Neural Networks (RNNs).
Markov Chains assume that a user's next action can be predicted on the basis of
just their last (or last few) actions, while RNNs in principle allow for
longer-term semantics to be uncovered. Generally speaking, MC-based methods
perform best in extremely sparse datasets, where model parsimony is critical,
while RNNs perform better in denser datasets where higher model complexity is
affordable. The goal of our work is to balance these two goals, by proposing a
self-attention based sequential model (SASRec) that allows us to capture
long-term semantics (like an RNN), but, using an attention mechanism, makes its
predictions based on relatively few actions (like an MC). At each time step,
SASRec seeks to identify which items are `relevant' from a user's action
history, and use them to predict the next item. Extensive empirical studies
show that our method outperforms various state-of-the-art sequential models
(including MC/CNN/RNN-based approaches) on both sparse and dense datasets.
Moreover, the model is an order of magnitude more efficient than comparable
CNN/RNN-based models. Visualizations on attention weights also show how our
model adaptively handles datasets with various density, and uncovers meaningful
patterns in activity sequences.
| true | true |
Kang, Wang-Cheng and McAuley, Julian
| 2,018 | null | null | null | null |
Self-Attentive Sequential Recommendation
|
Self Attention on Recommendation System - Jeffery chiang
|
https://medium.com/analytics-vidhya/self-attention-on-recommendation-system-self-attentive-sequential-recommendation-review-c94796dde001
|
Self-attention is a powerful mechanism used in deep learning to process sequential data, such as sentences or time-series data, by considering the relationship
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
bert4rec
|
\cite{bert4rec}
|
BERT4Rec: Sequential Recommendation with Bidirectional Encoder
Representations from Transformer
|
http://arxiv.org/abs/1904.06690v2
|
Modeling users' dynamic and evolving preferences from their historical
behaviors is challenging and crucial for recommendation systems. Previous
methods employ sequential neural networks (e.g., Recurrent Neural Network) to
encode users' historical interactions from left to right into hidden
representations for making recommendations. Although these methods achieve
satisfactory results, they often assume a rigidly ordered sequence which is not
always practical. We argue that such left-to-right unidirectional architectures
restrict the power of the historical sequence representations. For this
purpose, we introduce a Bidirectional Encoder Representations from Transformers
for sequential Recommendation (BERT4Rec). However, jointly conditioning on both
left and right context in deep bidirectional model would make the training
become trivial since each item can indirectly "see the target item". To address
this problem, we train the bidirectional model using the Cloze task, predicting
the masked items in the sequence by jointly conditioning on their left and
right context. Comparing with predicting the next item at each position in a
sequence, the Cloze task can produce more samples to train a more powerful
bidirectional model. Extensive experiments on four benchmark datasets show that
our model outperforms various state-of-the-art sequential models consistently.
| true | true |
Sun, Fei and Liu, Jun and Wu, Jian and Pei, Changhua and Lin, Xiao and Ou, Wenwu and Jiang, Peng
| 2,019 | null | null | null | null |
BERT4Rec: Sequential Recommendation with Bidirectional Encoder
Representations from Transformer
|
BERT4Rec: Sequential Recommendation with Bidirectional Encoder ...
|
https://dl.acm.org/doi/10.1145/3357384.3357895
|
We proposed a sequential recommendation model called BERT4Rec, which employs the deep bidirectional self-attention to model user behavior sequences.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
Linrec
|
\cite{Linrec}
|
LinRec: Linear Attention Mechanism for Long-term Sequential Recommender
Systems
|
http://arxiv.org/abs/2411.01537v1
|
Transformer models have achieved remarkable success in sequential recommender
systems (SRSs). However, computing the attention matrix in traditional
dot-product attention mechanisms results in a quadratic complexity with
sequence lengths, leading to high computational costs for long-term sequential
recommendation. Motivated by the above observation, we propose a novel
L2-Normalized Linear Attention for the Transformer-based Sequential Recommender
Systems (LinRec), which theoretically improves efficiency while preserving the
learning capabilities of the traditional dot-product attention. Specifically,
by thoroughly examining the equivalence conditions of efficient attention
mechanisms, we show that LinRec possesses linear complexity while preserving
the property of attention mechanisms. In addition, we reveal its latent
efficiency properties by interpreting the proposed LinRec mechanism through a
statistical lens. Extensive experiments are conducted based on two public
benchmark datasets, demonstrating that the combination of LinRec and
Transformer models achieves comparable or even superior performance than
state-of-the-art Transformer-based SRS models while significantly improving
time and memory efficiency.
| true | true |
Liu, Langming and Cai, Liu and Zhang, Chi and Zhao, Xiangyu and Gao, Jingtong and Wang, Wanyu and Lv, Yifu and Fan, Wenqi and Wang, Yiqi and He, Ming and others
| 2,023 | null | null | null | null |
LinRec: Linear Attention Mechanism for Long-term Sequential Recommender
Systems
|
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for ...
|
https://www.atailab.cn/seminar2025Spring/pdf/2025_KDD_GLINT-RU_Gated%20Lightweight%20Intelligent%20Recurrent%20Units%20for%20Sequential%20Recommender%20Systems.pdf
|
by S Zhang · 2025 · Cited by 6 — Linrec: Linear attention mechanism for long-term sequential recommender systems. In Proceedings of the 46th International ACM SIGIR Conference on Research
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
GRU4Rec
|
\cite{GRU4Rec}
|
Session-based Recommendations with Recurrent Neural Networks
|
http://arxiv.org/abs/1511.06939v4
|
We apply recurrent neural networks (RNN) on a new domain, namely recommender
systems. Real-life recommender systems often face the problem of having to base
recommendations only on short session-based data (e.g. a small sportsware
website) instead of long user histories (as in the case of Netflix). In this
situation the frequently praised matrix factorization approaches are not
accurate. This problem is usually overcome in practice by resorting to
item-to-item recommendations, i.e. recommending similar items. We argue that by
modeling the whole session, more accurate recommendations can be provided. We
therefore propose an RNN-based approach for session-based recommendations. Our
approach also considers practical aspects of the task and introduces several
modifications to classic RNNs such as a ranking loss function that make it more
viable for this specific problem. Experimental results on two data-sets show
marked improvements over widely used approaches.
| true | true |
Hidasi, Bal{\'a}zs and Karatzoglou, Alexandros and Baltrunas, Linas and Tikk, Domonkos
| 2,015 | null | null | null |
arXiv preprint arXiv:1511.06939
|
Session-based Recommendations with Recurrent Neural Networks
|
Session-based Recommendations with Recurrent Neural Networks
|
https://www.semanticscholar.org/paper/Session-based-Recommendations-with-Recurrent-Neural-Hidasi-Karatzoglou/e0021d61c2ab1334bc725852edd44597f4c65dff
|
It is argued that by modeling the whole session, more accurate recommendations can be provided by an RNN-based approach for session-based recommendations,
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
GLINTours25
|
\cite{GLINTours25}
|
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
| null | null | true | false |
Zhang, Sheng and Wang, Maolin and Zhao, Xiangyu
| 2,024 | null | null | null |
arXiv preprint arXiv:2406.10244
|
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
|
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
|
http://arxiv.org/pdf/2406.10244v3
|
Transformer-based models have gained significant traction in sequential
recommender systems (SRSs) for their ability to capture user-item interactions
effectively. However, these models often suffer from high computational costs
and slow inference. Meanwhile, existing efficient SRS approaches struggle to
embed high-quality semantic and positional information into latent
representations. To tackle these challenges, this paper introduces GLINT-RU, a
lightweight and efficient SRS leveraging a single-layer dense selective Gated
Recurrent Units (GRU) module to accelerate inference. By incorporating a dense
selective gate, GLINT-RU adaptively captures temporal dependencies and
fine-grained positional information, generating high-quality latent
representations. Additionally, a parallel mixing block infuses fine-grained
positional features into user-item interactions, enhancing both recommendation
quality and efficiency. Extensive experiments on three datasets demonstrate
that GLINT-RU achieves superior prediction accuracy and inference speed,
outperforming baselines based on RNNs, Transformers, MLPs, and SSMs. These
results establish GLINT-RU as a powerful and efficient solution for SRSs.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
HiPPOs21
|
\cite{HiPPOs21}
|
There is HOPE to Avoid HiPPOs for Long-memory State Space Models
| null | null | true | false |
Yu, Annan and Mahoney, Michael W and Erichson, N Benjamin
| 2,024 | null | null | null |
arXiv preprint arXiv:2405.13975
|
There is HOPE to Avoid HiPPOs for Long-memory State Space Models
|
There is HOPE to Avoid HiPPOs for Long-memory State ...
|
https://www.researchgate.net/publication/380820131_There_is_HOPE_to_Avoid_HiPPOs_for_Long-memory_State_Space_Models
|
State-space models (SSMs) that utilize linear, time-invariant (LTI) systems are known for their effectiveness in learning long sequences.See more
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
16Dual
|
\cite{16Dual}
|
Dual-path Mamba: Short and Long-term Bidirectional Selective Structured
State Space Models for Speech Separation
|
http://arxiv.org/abs/2403.18257v2
|
Transformers have been the most successful architecture for various speech
modeling tasks, including speech separation. However, the self-attention
mechanism in transformers with quadratic complexity is inefficient in
computation and memory. Recent models incorporate new layers and modules along
with transformers for better performance but also introduce extra model
complexity. In this work, we replace transformers with Mamba, a selective state
space model, for speech separation. We propose dual-path Mamba, which models
short-term and long-term forward and backward dependency of speech signals
using selective state spaces. Our experimental results on the WSJ0-2mix data
show that our dual-path Mamba models of comparably smaller sizes outperform
state-of-the-art RNN model DPRNN, CNN model WaveSplit, and transformer model
Sepformer. Code: https://github.com/xi-j/Mamba-TasNet
| true | true |
Jiang, Xilin and Han, Cong and Mesgarani, Nima
| 2,024 | null | null | null |
arXiv preprint arXiv:2403.18257
|
Dual-path Mamba: Short and Long-term Bidirectional Selective Structured
State Space Models for Speech Separation
|
Dual-path Mamba: Short and Long-term Bidirectional Selective ...
|
https://arxiv.org/abs/2403.18257
|
We propose dual-path Mamba, which models short-term and long-term forward and backward dependency of speech signals using selective state spaces.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
gu2023mamba
|
\cite{gu2023mamba}
|
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
|
http://arxiv.org/abs/2312.00752v2
|
Foundation models, now powering most of the exciting applications in deep
learning, are almost universally based on the Transformer architecture and its
core attention module. Many subquadratic-time architectures such as linear
attention, gated convolution and recurrent models, and structured state space
models (SSMs) have been developed to address Transformers' computational
inefficiency on long sequences, but they have not performed as well as
attention on important modalities such as language. We identify that a key
weakness of such models is their inability to perform content-based reasoning,
and make several improvements. First, simply letting the SSM parameters be
functions of the input addresses their weakness with discrete modalities,
allowing the model to selectively propagate or forget information along the
sequence length dimension depending on the current token. Second, even though
this change prevents the use of efficient convolutions, we design a
hardware-aware parallel algorithm in recurrent mode. We integrate these
selective SSMs into a simplified end-to-end neural network architecture without
attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5$\times$
higher throughput than Transformers) and linear scaling in sequence length, and
its performance improves on real data up to million-length sequences. As a
general sequence model backbone, Mamba achieves state-of-the-art performance
across several modalities such as language, audio, and genomics. On language
modeling, our Mamba-3B model outperforms Transformers of the same size and
matches Transformers twice its size, both in pretraining and downstream
evaluation.
| true | true |
Gu, Albert and Dao, Tri
| 2,023 | null | null | null |
arXiv preprint arXiv:2312.00752
|
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
|
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
|
https://openreview.net/forum?id=tEYskw1VY2
|
This paper proposes Mamba, a linear-time sequence model with an intra-layer combination of Selective S4D, Short Convolution and Gated Linear Unit. The paper
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
qu2024survey
|
\cite{qu2024survey}
|
A Survey of Mamba
|
http://arxiv.org/abs/2408.01129v6
|
As one of the most representative DL techniques, Transformer architecture has
empowered numerous advanced models, especially the large language models (LLMs)
that comprise billions of parameters, becoming a cornerstone in deep learning.
Despite the impressive achievements, Transformers still face inherent
limitations, particularly the time-consuming inference resulting from the
quadratic computation complexity of attention calculation. Recently, a novel
architecture named Mamba, drawing inspiration from classical state space models
(SSMs), has emerged as a promising alternative for building foundation models,
delivering comparable modeling abilities to Transformers while preserving
near-linear scalability concerning sequence length. This has sparked an
increasing number of studies actively exploring Mamba's potential to achieve
impressive performance across diverse domains. Given such rapid evolution,
there is a critical need for a systematic review that consolidates existing
Mamba-empowered models, offering a comprehensive understanding of this emerging
model architecture. In this survey, we therefore conduct an in-depth
investigation of recent Mamba-associated studies, covering three main aspects:
the advancements of Mamba-based models, the techniques of adapting Mamba to
diverse data, and the applications where Mamba can excel. Specifically, we
first review the foundational knowledge of various representative deep learning
models and the details of Mamba-1&2 as preliminaries. Then, to showcase the
significance of Mamba for AI, we comprehensively review the related studies
focusing on Mamba models' architecture design, data adaptability, and
applications. Finally, we present a discussion of current limitations and
explore various promising research directions to provide deeper insights for
future investigations.
| true | true |
Qu, Haohao and Ning, Liangbo and An, Rui and Fan, Wenqi and Derr, Tyler and Liu, Hui and Xu, Xin and Li, Qing
| 2,024 | null | null | null |
arXiv preprint arXiv:2408.01129
|
A Survey of Mamba
|
A Survey of Mamba
|
http://arxiv.org/pdf/2408.01129v6
|
As one of the most representative DL techniques, Transformer architecture has
empowered numerous advanced models, especially the large language models (LLMs)
that comprise billions of parameters, becoming a cornerstone in deep learning.
Despite the impressive achievements, Transformers still face inherent
limitations, particularly the time-consuming inference resulting from the
quadratic computation complexity of attention calculation. Recently, a novel
architecture named Mamba, drawing inspiration from classical state space models
(SSMs), has emerged as a promising alternative for building foundation models,
delivering comparable modeling abilities to Transformers while preserving
near-linear scalability concerning sequence length. This has sparked an
increasing number of studies actively exploring Mamba's potential to achieve
impressive performance across diverse domains. Given such rapid evolution,
there is a critical need for a systematic review that consolidates existing
Mamba-empowered models, offering a comprehensive understanding of this emerging
model architecture. In this survey, we therefore conduct an in-depth
investigation of recent Mamba-associated studies, covering three main aspects:
the advancements of Mamba-based models, the techniques of adapting Mamba to
diverse data, and the applications where Mamba can excel. Specifically, we
first review the foundational knowledge of various representative deep learning
models and the details of Mamba-1&2 as preliminaries. Then, to showcase the
significance of Mamba for AI, we comprehensively review the related studies
focusing on Mamba models' architecture design, data adaptability, and
applications. Finally, we present a discussion of current limitations and
explore various promising research directions to provide deeper insights for
future investigations.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
dao2024transformers
|
\cite{dao2024transformers}
|
Transformers are SSMs: Generalized Models and Efficient Algorithms
Through Structured State Space Duality
|
http://arxiv.org/abs/2405.21060v1
|
While Transformers have been the main architecture behind deep learning's
success in language modeling, state-space models (SSMs) such as Mamba have
recently been shown to match or outperform Transformers at small to medium
scale. We show that these families of models are actually quite closely
related, and develop a rich framework of theoretical connections between SSMs
and variants of attention, connected through various decompositions of a
well-studied class of structured semiseparable matrices. Our state space
duality (SSD) framework allows us to design a new architecture (Mamba-2) whose
core layer is an a refinement of Mamba's selective SSM that is 2-8X faster,
while continuing to be competitive with Transformers on language modeling.
| true | true |
Dao, Tri and Gu, Albert
| 2,024 | null | null | null |
arXiv preprint arXiv:2405.21060
|
Transformers are SSMs: Generalized Models and Efficient Algorithms
Through Structured State Space Duality
|
Transformers are SSMs: Generalized Models and Efficient ...
|
https://openreview.net/pdf/54bf495d93336f1f195f264c1b6c2805169b3492.pdf
|
27 Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality D.3.3 FULLY RECURRENT MODE Note that the fully recurrent mode, where the recurrence is evolved one step at a time (15), is simply an instantiation of the state-passing mode with chunk size k=1.
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
MambaRec
|
\cite{MambaRec}
|
Uncovering Selective State Space Model's Capabilities in Lifelong
Sequential Recommendation
|
http://arxiv.org/abs/2403.16371v1
|
Sequential Recommenders have been widely applied in various online services,
aiming to model users' dynamic interests from their sequential interactions.
With users increasingly engaging with online platforms, vast amounts of
lifelong user behavioral sequences have been generated. However, existing
sequential recommender models often struggle to handle such lifelong sequences.
The primary challenges stem from computational complexity and the ability to
capture long-range dependencies within the sequence. Recently, a state space
model featuring a selective mechanism (i.e., Mamba) has emerged. In this work,
we investigate the performance of Mamba for lifelong sequential recommendation
(i.e., length>=2k). More specifically, we leverage the Mamba block to model
lifelong user sequences selectively. We conduct extensive experiments to
evaluate the performance of representative sequential recommendation models in
the setting of lifelong sequences. Experiments on two real-world datasets
demonstrate the superiority of Mamba. We found that RecMamba achieves
performance comparable to the representative model while significantly reducing
training duration by approximately 70% and memory costs by 80%. Codes and data
are available at \url{https://github.com/nancheng58/RecMamba}.
| true | true |
Yang, Jiyuan and Li, Yuanzi and Zhao, Jingyu and Wang, Hanbing and Ma, Muyang and Ma, Jun and Ren, Zhaochun and Zhang, Mengqi and Xin, Xin and Chen, Zhumin and others
| 2,024 | null | null | null |
arXiv preprint arXiv:2403.16371
|
Uncovering Selective State Space Model's Capabilities in Lifelong
Sequential Recommendation
|
[PDF] Uncovering Selective State Space Model's Capabilities in Lifelong ...
|
https://arxiv.org/pdf/2403.16371
|
We conduct extensive ex- periments to evaluate the performance of representative sequential recommendation models in the setting of lifelong
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
wang2024echomamba4rec
|
\cite{wang2024echomamba4rec}
|
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with
Spectral Filtering for Advanced Sequential Recommendation
|
http://arxiv.org/abs/2406.02638v2
|
Predicting user preferences and sequential dependencies based on historical
behavior is the core goal of sequential recommendation. Although
attention-based models have shown effectiveness in this field, they often
struggle with inference inefficiency due to the quadratic computational
complexity inherent in attention mechanisms, especially with long-range
behavior sequences. Drawing inspiration from the recent advancements of state
space models (SSMs) in control theory, which provide a robust framework for
modeling and controlling dynamic systems, we introduce EchoMamba4Rec. Control
theory emphasizes the use of SSMs for managing long-range dependencies and
maintaining inferential efficiency through structured state matrices.
EchoMamba4Rec leverages these control relationships in sequential
recommendation and integrates bi-directional processing with frequency-domain
filtering to capture complex patterns and dependencies in user interaction data
more effectively. Our model benefits from the ability of state space models
(SSMs) to learn and perform parallel computations, significantly enhancing
computational efficiency and scalability. It features a bi-directional Mamba
module that incorporates both forward and reverse Mamba components, leveraging
information from both past and future interactions. Additionally, a filter
layer operates in the frequency domain using learnable Fast Fourier Transform
(FFT) and learnable filters, followed by an inverse FFT to refine item
embeddings and reduce noise. We also integrate Gate Linear Units (GLU) to
dynamically control information flow, enhancing the model's expressiveness and
training stability. Experimental results demonstrate that EchoMamba
significantly outperforms existing models, providing more accurate and
personalized recommendations.
| true | true |
Wang, Yuda and He, Xuxin and Zhu, Shengxin
| 2,024 | null | null | null |
arXiv preprint arXiv:2406.02638
|
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with
Spectral Filtering for Advanced Sequential Recommendation
|
EchoMamba4Rec: Harmonizing Bidirectional State Space ...
|
https://www.researchgate.net/publication/381190112_EchoMamba4Rec_Harmonizing_Bidirectional_State_Space_Models_with_Spectral_Filtering_for_Advanced_Sequential_Recommendation
|
EchoMamba4Rec leverages these control relationships in sequential recommendation and integrates bi-directional processing with frequency-domain
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
cao2024mamba4kt
|
\cite{cao2024mamba4kt}
|
Mamba4KT:An Efficient and Effective Mamba-based Knowledge Tracing Model
|
http://arxiv.org/abs/2405.16542v1
|
Knowledge tracing (KT) enhances student learning by leveraging past
performance to predict future performance. Current research utilizes models
based on attention mechanisms and recurrent neural network structures to
capture long-term dependencies and correlations between exercises, aiming to
improve model accuracy. Due to the growing amount of data in smart education
scenarios, this poses a challenge in terms of time and space consumption for
knowledge tracing models. However, existing research often overlooks the
efficiency of model training and inference and the constraints of training
resources. Recognizing the significance of prioritizing model efficiency and
resource usage in knowledge tracing, we introduce Mamba4KT. This novel model is
the first to explore enhanced efficiency and resource utilization in knowledge
tracing. We also examine the interpretability of the Mamba structure both
sequence-level and exercise-level to enhance model interpretability.
Experimental findings across three public datasets demonstrate that Mamba4KT
achieves comparable prediction accuracy to state-of-the-art models while
significantly improving training and inference efficiency and resource
utilization. As educational data continues to grow, our work suggests a
promising research direction for knowledge tracing that improves model
prediction accuracy, model efficiency, resource utilization, and
interpretability simultaneously.
| true | true |
Cao, Yang and Zhang, Wei
| 2,024 | null | null | null |
arXiv preprint arXiv:2405.16542
|
Mamba4KT:An Efficient and Effective Mamba-based Knowledge Tracing Model
|
Mamba4KT:An Efficient and Effective Mamba-based ...
|
https://arxiv.org/html/2405.16542v1
|
We introduce a knowledge tracing model Mamba4KT based on selective state space model, which improves the training and inference efficiency and
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
liu2024bidirectional
|
\cite{liu2024bidirectional}
|
Bidirectional gated mamba for sequential recommendation
| null | null | true | false |
Liu, Ziwei and Liu, Qidong and Wang, Yejing and Wang, Wanyu and Jia, Pengyue and Wang, Maolin and Liu, Zitao and Chang, Yi and Zhao, Xiangyu
| 2,024 | null | null | null |
arXiv preprint arXiv:2408.11451
|
Bidirectional gated mamba for sequential recommendation
|
Bidirectional Gated Mamba for Sequential Recommendation
|
https://openreview.net/forum?id=xaJx6aRwRG
|
Bidirectional Gated Mamba for Sequential Recommendation | OpenReview Bidirectional Gated Mamba for Sequential Recommendation To overcome these issues, we introduce a new framework named Selective Gated Mamba (SIGMA) for Sequential Recommendation. This framework leverages a Partially Flipped Mamba (PF-Mamba) to construct a bidirectional architecture specifically tailored to improve contextual modeling. Additionally, an input-sensitive Dense Selective Gate (DS Gate) is employed to optimize directional weights and enhance the processing of sequential information in PF-Mamba. * About OpenReview To submit a bug report or feature request, you can use the official OpenReview GitHub repository: * About OpenReview To submit a bug report or feature request, you can use the official OpenReview GitHub repository:
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
yang2024uncovering
|
\cite{yang2024uncovering}
|
Uncovering Selective State Space Model's Capabilities in Lifelong
Sequential Recommendation
|
http://arxiv.org/abs/2403.16371v1
|
Sequential Recommenders have been widely applied in various online services,
aiming to model users' dynamic interests from their sequential interactions.
With users increasingly engaging with online platforms, vast amounts of
lifelong user behavioral sequences have been generated. However, existing
sequential recommender models often struggle to handle such lifelong sequences.
The primary challenges stem from computational complexity and the ability to
capture long-range dependencies within the sequence. Recently, a state space
model featuring a selective mechanism (i.e., Mamba) has emerged. In this work,
we investigate the performance of Mamba for lifelong sequential recommendation
(i.e., length>=2k). More specifically, we leverage the Mamba block to model
lifelong user sequences selectively. We conduct extensive experiments to
evaluate the performance of representative sequential recommendation models in
the setting of lifelong sequences. Experiments on two real-world datasets
demonstrate the superiority of Mamba. We found that RecMamba achieves
performance comparable to the representative model while significantly reducing
training duration by approximately 70% and memory costs by 80%. Codes and data
are available at \url{https://github.com/nancheng58/RecMamba}.
| true | true |
Yang, Jiyuan and Li, Yuanzi and Zhao, Jingyu and Wang, Hanbing and Ma, Muyang and Ma, Jun and Ren, Zhaochun and Zhang, Mengqi and Xin, Xin and Chen, Zhumin and others
| 2,024 | null | null | null |
arXiv preprint arXiv:2403.16371
|
Uncovering Selective State Space Model's Capabilities in Lifelong
Sequential Recommendation
|
[PDF] Uncovering Selective State Space Model's Capabilities in Lifelong ...
|
https://arxiv.org/pdf/2403.16371
|
We conduct extensive ex- periments to evaluate the performance of representative sequential recommendation models in the setting of lifelong
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
Visionzhu
|
\cite{Visionzhu}
|
Vision Mamba: Efficient Visual Representation Learning with
Bidirectional State Space Model
|
http://arxiv.org/abs/2401.09417v3
|
Recently the state space models (SSMs) with efficient hardware-aware designs,
i.e., the Mamba deep learning model, have shown great potential for long
sequence modeling. Meanwhile building efficient and generic vision backbones
purely upon SSMs is an appealing direction. However, representing visual data
is challenging for SSMs due to the position-sensitivity of visual data and the
requirement of global context for visual understanding. In this paper, we show
that the reliance on self-attention for visual representation learning is not
necessary and propose a new generic vision backbone with bidirectional Mamba
blocks (Vim), which marks the image sequences with position embeddings and
compresses the visual representation with bidirectional state space models. On
ImageNet classification, COCO object detection, and ADE20k semantic
segmentation tasks, Vim achieves higher performance compared to
well-established vision transformers like DeiT, while also demonstrating
significantly improved computation & memory efficiency. For example, Vim is
2.8$\times$ faster than DeiT and saves 86.8% GPU memory when performing batch
inference to extract features on images with a resolution of 1248$\times$1248.
The results demonstrate that Vim is capable of overcoming the computation &
memory constraints on performing Transformer-style understanding for
high-resolution images and it has great potential to be the next-generation
backbone for vision foundation models. Code is available at
https://github.com/hustvl/Vim.
| true | true |
Zhu, Lianghui and Liao, Bencheng and Zhang, Qian and Wang, Xinlong and Liu, Wenyu and Wang, Xinggang
| 2,024 | null | null | null |
arXiv preprint arXiv:2401.09417
|
Vision Mamba: Efficient Visual Representation Learning with
Bidirectional State Space Model
|
Vision Mamba: Efficient Visual Representation Learning with ... - arXiv
|
https://arxiv.org/abs/2401.09417
|
Title:Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model View a PDF of the paper titled Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model, by Lianghui Zhu and 5 other authors In this paper, we show that the reliance on self-attention for visual representation learning is not necessary and propose a new generic vision backbone with bidirectional Mamba blocks (Vim), which marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models. View a PDF of the paper titled Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model, by Lianghui Zhu and 5 other authors - [x] Connected Papers Toggle - [x] Links to Code Toggle - [x] Links to Code Toggle
|
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in
Sequential Recommendation
|
2505.03484v1
|
mamba4rec
|
\cite{mamba4rec}
|
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective
State Space Models
|
http://arxiv.org/abs/2403.03900v2
|
Sequential recommendation aims to estimate the dynamic user preferences and
sequential dependencies among historical user behaviors. Although
Transformer-based models have proven to be effective for sequential
recommendation, they suffer from the inference inefficiency problem stemming
from the quadratic computational complexity of attention operators, especially
for long behavior sequences. Inspired by the recent success of state space
models (SSMs), we propose Mamba4Rec, which is the first work to explore the
potential of selective SSMs for efficient sequential recommendation. Built upon
the basic Mamba block which is a selective SSM with an efficient hardware-aware
parallel algorithm, we design a series of sequential modeling techniques to
further promote model performance while maintaining inference efficiency.
Through experiments on public datasets, we demonstrate how Mamba4Rec
effectively tackles the effectiveness-efficiency dilemma, outperforming both
RNN- and attention-based baselines in terms of both effectiveness and
efficiency. The code is available at https://github.com/chengkai-liu/Mamba4Rec.
| true | true |
Liu, Chengkai and Lin, Jianghao and Wang, Jianling and Liu, Hanzhou and Caverlee, James
| 2,024 | null | null | null |
arXiv preprint arXiv:2403.03900
|
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective
State Space Models
|
Towards Efficient Sequential Recommendation with ...
|
https://arxiv.org/pdf/2403.03900
|
by C Liu · 2024 · Cited by 66 — We describe how. Mamba4Rec constructs a sequential recommendation model through an embedding layer, selective state space models, and a prediction layer.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
perozziDeepwalk2014
|
\cite{perozziDeepwalk2014}
|
Deep{W}alk: Online learning of social representations
| null | null | true | false |
Perozzi, Bryan and Al-Rfou, Rami and Skiena, Steven
| 2,014 | null | null | null | null |
Deep{W}alk: Online learning of social representations
|
DeepWalk: online learning of social representations
|
https://dl.acm.org/doi/10.1145/2623330.2623732
|
We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
groverNode2vecScalableFeature2016
|
\cite{groverNode2vecScalableFeature2016}
|
node2vec: Scalable Feature Learning for Networks
|
http://arxiv.org/abs/1607.00653v1
|
Prediction tasks over nodes and edges in networks require careful effort in
engineering features used by learning algorithms. Recent research in the
broader field of representation learning has led to significant progress in
automating prediction by learning the features themselves. However, present
feature learning approaches are not expressive enough to capture the diversity
of connectivity patterns observed in networks. Here we propose node2vec, an
algorithmic framework for learning continuous feature representations for nodes
in networks. In node2vec, we learn a mapping of nodes to a low-dimensional
space of features that maximizes the likelihood of preserving network
neighborhoods of nodes. We define a flexible notion of a node's network
neighborhood and design a biased random walk procedure, which efficiently
explores diverse neighborhoods. Our algorithm generalizes prior work which is
based on rigid notions of network neighborhoods, and we argue that the added
flexibility in exploring neighborhoods is the key to learning richer
representations. We demonstrate the efficacy of node2vec over existing
state-of-the-art techniques on multi-label classification and link prediction
in several real-world networks from diverse domains. Taken together, our work
represents a new way for efficiently learning state-of-the-art task-independent
representations in complex networks.
| true | true |
Grover, Aditya and Leskovec, Jure
| 2,016 | null | null | null | null |
node2vec: Scalable Feature Learning for Networks
|
node2vec: Scalable Feature Learning for Networks
|
http://arxiv.org/pdf/1607.00653v1
|
Prediction tasks over nodes and edges in networks require careful effort in
engineering features used by learning algorithms. Recent research in the
broader field of representation learning has led to significant progress in
automating prediction by learning the features themselves. However, present
feature learning approaches are not expressive enough to capture the diversity
of connectivity patterns observed in networks. Here we propose node2vec, an
algorithmic framework for learning continuous feature representations for nodes
in networks. In node2vec, we learn a mapping of nodes to a low-dimensional
space of features that maximizes the likelihood of preserving network
neighborhoods of nodes. We define a flexible notion of a node's network
neighborhood and design a biased random walk procedure, which efficiently
explores diverse neighborhoods. Our algorithm generalizes prior work which is
based on rigid notions of network neighborhoods, and we argue that the added
flexibility in exploring neighborhoods is the key to learning richer
representations. We demonstrate the efficacy of node2vec over existing
state-of-the-art techniques on multi-label classification and link prediction
in several real-world networks from diverse domains. Taken together, our work
represents a new way for efficiently learning state-of-the-art task-independent
representations in complex networks.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
huangGraphRecurrentNetworks2019
|
\cite{huangGraphRecurrentNetworks2019}
|
Graph recurrent networks with attributed random walks
| null | null | true | false |
Huang, Xiao and Song, Qingquan and Li, Yuening and Hu, Xia
| 2,019 | null | null | null | null |
Graph recurrent networks with attributed random walks
|
[PDF] Attributed Random Walks for Graph Recurrent Networks
|
https://www4.comp.polyu.edu.hk/~xiaohuang/docs/Xiao_KDD19_slides.pdf
|
Apply random walks on attributed networks to boost deep node representation learning. boost. Page 7. Graph Recurrent Networks with Attributed. Random Walks ……
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
nikolentzosRandomwalkgraphneuralnetworks2020
|
\cite{nikolentzosRandomwalkgraphneuralnetworks2020}
|
Random walk graph neural networks
| null | null | true | false |
Nikolentzos, Giannis and Vazirgiannis, Michalis
| 2,020 | null | null | null | null |
Random walk graph neural networks
|
Random Walk Graph Neural Networks
|
https://proceedings.neurips.cc/paper/2020/file/ba95d78a7c942571185308775a97a3a0-Paper.pdf
|
by G Nikolentzos · 2020 · Cited by 160 — In this paper, we propose a more intuitive and transparent architecture for graph-structured data, so-called Random Walk. Graph Neural Network (RWNN). The first
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
jinRawgnn2022
|
\cite{jinRawgnn2022}
|
Raw-{GNN}: Random walk aggregation based graph neural network
| null | null | true | false |
Jin, Di and Wang, Rui and Ge, Meng and He, Dongxiao and Li, Xiang and Lin, Wei and Zhang, Weixiong
| 2,022 | null | null | null |
arXiv:2206.13953
|
Raw-{GNN}: Random walk aggregation based graph neural network
|
RAndom Walk Aggregation based Graph Neural Network
|
https://www.ijcai.org/proceedings/2022/0293.pdf
|
by D Jin · Cited by 59 — Here, we introduce a novel aggregation mechanism and develop a RAn- dom Walk Aggregation-based Graph Neural Net- work (called RAW-GNN) method. The proposed.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
wangNonConvGNN2024
|
\cite{wangNonConvGNN2024}
|
Non-convolutional Graph Neural Networks
|
http://arxiv.org/abs/2408.00165v3
|
Rethink convolution-based graph neural networks (GNN) -- they
characteristically suffer from limited expressiveness, over-smoothing, and
over-squashing, and require specialized sparse kernels for efficient
computation. Here, we design a simple graph learning module entirely free of
convolution operators, coined random walk with unifying memory (RUM) neural
network, where an RNN merges the topological and semantic graph features along
the random walks terminating at each node. Relating the rich literature on RNN
behavior and graph topology, we theoretically show and experimentally verify
that RUM attenuates the aforementioned symptoms and is more expressive than the
Weisfeiler-Lehman (WL) isomorphism test. On a variety of node- and graph-level
classification and regression tasks, RUM not only achieves competitive
performance, but is also robust, memory-efficient, scalable, and faster than
the simplest convolutional GNNs.
| true | true |
Wang, Yuanqing and Cho, Kyunghyun
| 2,024 | null | null | null |
arXiv:2408.00165
|
Non-convolutional Graph Neural Networks
|
[2408.00165] Non-convolutional Graph Neural Networks
|
https://arxiv.org/abs/2408.00165
|
by Y Wang · 2024 · Cited by 12 — We design a simple graph learning module entirely free of convolution operators, coined random walk with unifying memory (RUM) neural network.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
kipfSemiSupervisedClassificationGraph2017
|
\cite{kipfSemiSupervisedClassificationGraph2017}
|
Semi-Supervised Classification with Graph Convolutional Networks
|
http://arxiv.org/abs/1609.02907v4
|
We present a scalable approach for semi-supervised learning on
graph-structured data that is based on an efficient variant of convolutional
neural networks which operate directly on graphs. We motivate the choice of our
convolutional architecture via a localized first-order approximation of
spectral graph convolutions. Our model scales linearly in the number of graph
edges and learns hidden layer representations that encode both local graph
structure and features of nodes. In a number of experiments on citation
networks and on a knowledge graph dataset we demonstrate that our approach
outperforms related methods by a significant margin.
| true | true |
Kipf, Thomas N and Welling, Max
| 2,016 | null | null | null | null |
Semi-Supervised Classification with Graph Convolutional Networks
|
Semi-Supervised Classification with Graph Convolutional Networks
|
https://openreview.net/forum?id=SJU4ayYgl
|
Semi-Supervised Classification with Graph Convolutional Networks | OpenReview Semi-Supervised Classification with Graph Convolutional Networks Abstract:We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. TL;DR:Semi-supervised classification with a CNN model for graphs. About OpenReview To submit a bug report or feature request, you can use the official OpenReview GitHub repository: Select a topic or type what you need help with Cancel Send * Sponsors About OpenReview Sponsors To submit a bug report or feature request, you can use the official OpenReview GitHub repository: Select a topic or type what you need help with Cancel Send We gratefully acknowledge the support of theOpenReview Sponsors.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
wuSimplifyingGraphConvolutional2019
|
\cite{wuSimplifyingGraphConvolutional2019}
|
Simplifying Graph Convolutional Networks
|
http://arxiv.org/abs/1902.07153v2
|
Graph Convolutional Networks (GCNs) and their variants have experienced
significant attention and have become the de facto methods for learning graph
representations. GCNs derive inspiration primarily from recent deep learning
approaches, and as a result, may inherit unnecessary complexity and redundant
computation. In this paper, we reduce this excess complexity through
successively removing nonlinearities and collapsing weight matrices between
consecutive layers. We theoretically analyze the resulting linear model and
show that it corresponds to a fixed low-pass filter followed by a linear
classifier. Notably, our experimental evaluation demonstrates that these
simplifications do not negatively impact accuracy in many downstream
applications. Moreover, the resulting model scales to larger datasets, is
naturally interpretable, and yields up to two orders of magnitude speedup over
FastGCN.
| true | true |
Wu, Felix and Souza, Amauri and Zhang, Tianyi and Fifty, Christopher and Yu, Tao and Weinberger, Kilian
| 2,019 | null | null | null | null |
Simplifying Graph Convolutional Networks
|
Simplifying Graph Convolutional Networks
|
http://arxiv.org/pdf/1902.07153v2
|
Graph Convolutional Networks (GCNs) and their variants have experienced
significant attention and have become the de facto methods for learning graph
representations. GCNs derive inspiration primarily from recent deep learning
approaches, and as a result, may inherit unnecessary complexity and redundant
computation. In this paper, we reduce this excess complexity through
successively removing nonlinearities and collapsing weight matrices between
consecutive layers. We theoretically analyze the resulting linear model and
show that it corresponds to a fixed low-pass filter followed by a linear
classifier. Notably, our experimental evaluation demonstrates that these
simplifications do not negatively impact accuracy in many downstream
applications. Moreover, the resulting model scales to larger datasets, is
naturally interpretable, and yields up to two orders of magnitude speedup over
FastGCN.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
hamiltonInductiveRepresentationLearning2017
|
\cite{hamiltonInductiveRepresentationLearning2017}
|
Inductive Representation Learning in Large Attributed Graphs
|
http://arxiv.org/abs/1710.09471v2
|
Graphs (networks) are ubiquitous and allow us to model entities (nodes) and
the dependencies (edges) between them. Learning a useful feature representation
from graph data lies at the heart and success of many machine learning tasks
such as classification, anomaly detection, link prediction, among many others.
Many existing techniques use random walks as a basis for learning features or
estimating the parameters of a graph model for a downstream prediction task.
Examples include recent node embedding methods such as DeepWalk, node2vec, as
well as graph-based deep learning algorithms. However, the simple random walk
used by these methods is fundamentally tied to the identity of the node. This
has three main disadvantages. First, these approaches are inherently
transductive and do not generalize to unseen nodes and other graphs. Second,
they are not space-efficient as a feature vector is learned for each node which
is impractical for large graphs. Third, most of these approaches lack support
for attributed graphs.
To make these methods more generally applicable, we propose a framework for
inductive network representation learning based on the notion of attributed
random walk that is not tied to node identity and is instead based on learning
a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute
vector $\mathrm{\rm \bf x}$ to a type $w$. This framework serves as a basis for
generalizing existing methods such as DeepWalk, node2vec, and many other
previous methods that leverage traditional random walks.
| true | true |
Hamilton, Will and Ying, Zhitao and Leskovec, Jure
| 2,017 | null | null | null | null |
Inductive Representation Learning in Large Attributed Graphs
|
Inductive Representation Learning in Large Attributed Graphs
|
http://arxiv.org/pdf/1710.09471v2
|
Graphs (networks) are ubiquitous and allow us to model entities (nodes) and
the dependencies (edges) between them. Learning a useful feature representation
from graph data lies at the heart and success of many machine learning tasks
such as classification, anomaly detection, link prediction, among many others.
Many existing techniques use random walks as a basis for learning features or
estimating the parameters of a graph model for a downstream prediction task.
Examples include recent node embedding methods such as DeepWalk, node2vec, as
well as graph-based deep learning algorithms. However, the simple random walk
used by these methods is fundamentally tied to the identity of the node. This
has three main disadvantages. First, these approaches are inherently
transductive and do not generalize to unseen nodes and other graphs. Second,
they are not space-efficient as a feature vector is learned for each node which
is impractical for large graphs. Third, most of these approaches lack support
for attributed graphs.
To make these methods more generally applicable, we propose a framework for
inductive network representation learning based on the notion of attributed
random walk that is not tied to node identity and is instead based on learning
a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute
vector $\mathrm{\rm \bf x}$ to a type $w$. This framework serves as a basis for
generalizing existing methods such as DeepWalk, node2vec, and many other
previous methods that leverage traditional random walks.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
gilmerNeuralMessagePassing2017
|
\cite{gilmerNeuralMessagePassing2017}
|
Neural Message Passing for Quantum Chemistry
|
http://arxiv.org/abs/1704.01212v2
|
Supervised learning on molecules has incredible potential to be useful in
chemistry, drug discovery, and materials science. Luckily, several promising
and closely related neural network models invariant to molecular symmetries
have already been described in the literature. These models learn a message
passing algorithm and aggregation procedure to compute a function of their
entire input graph. At this point, the next step is to find a particularly
effective variant of this general approach and apply it to chemical prediction
benchmarks until we either solve them or reach the limits of the approach. In
this paper, we reformulate existing models into a single common framework we
call Message Passing Neural Networks (MPNNs) and explore additional novel
variations within this framework. Using MPNNs we demonstrate state of the art
results on an important molecular property prediction benchmark; these results
are strong enough that we believe future work should focus on datasets with
larger molecules or more accurate ground truth labels.
| true | true |
Gilmer, Justin and Schoenholz, Samuel S. and Riley, Patrick F. and Vinyals, Oriol and Dahl, George E.
| 2,017 | null | null | null | null |
Neural Message Passing for Quantum Chemistry
|
Neural Message Passing for Quantum Chemistry
|
http://arxiv.org/pdf/1704.01212v2
|
Supervised learning on molecules has incredible potential to be useful in
chemistry, drug discovery, and materials science. Luckily, several promising
and closely related neural network models invariant to molecular symmetries
have already been described in the literature. These models learn a message
passing algorithm and aggregation procedure to compute a function of their
entire input graph. At this point, the next step is to find a particularly
effective variant of this general approach and apply it to chemical prediction
benchmarks until we either solve them or reach the limits of the approach. In
this paper, we reformulate existing models into a single common framework we
call Message Passing Neural Networks (MPNNs) and explore additional novel
variations within this framework. Using MPNNs we demonstrate state of the art
results on an important molecular property prediction benchmark; these results
are strong enough that we believe future work should focus on datasets with
larger molecules or more accurate ground truth labels.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
velickovicDeepGraphInfomax2018
|
\cite{velickovicDeepGraphInfomax2018}
|
Deep Graph Infomax
|
http://arxiv.org/abs/1809.10341v2
|
We present Deep Graph Infomax (DGI), a general approach for learning node
representations within graph-structured data in an unsupervised manner. DGI
relies on maximizing mutual information between patch representations and
corresponding high-level summaries of graphs---both derived using established
graph convolutional network architectures. The learnt patch representations
summarize subgraphs centered around nodes of interest, and can thus be reused
for downstream node-wise learning tasks. In contrast to most prior approaches
to unsupervised learning with GCNs, DGI does not rely on random walk
objectives, and is readily applicable to both transductive and inductive
learning setups. We demonstrate competitive performance on a variety of node
classification benchmarks, which at times even exceeds the performance of
supervised learning.
| true | true |
Velickovic, Petar and Fedus, William and Hamilton, William L and Li{\`o}, Pietro and Bengio, Yoshua and Hjelm, R Devon
| 2,019 | null | null | null | null |
Deep Graph Infomax
|
[1809.10341] Deep Graph Infomax - arXiv
|
https://arxiv.org/abs/1809.10341
|
Abstract:We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
xuHowPowerfulAre2019
|
\cite{xuHowPowerfulAre2019}
|
How Powerful are Graph Neural Networks?
|
http://arxiv.org/abs/1810.00826v3
|
Graph Neural Networks (GNNs) are an effective framework for representation
learning of graphs. GNNs follow a neighborhood aggregation scheme, where the
representation vector of a node is computed by recursively aggregating and
transforming representation vectors of its neighboring nodes. Many GNN variants
have been proposed and have achieved state-of-the-art results on both node and
graph classification tasks. However, despite GNNs revolutionizing graph
representation learning, there is limited understanding of their
representational properties and limitations. Here, we present a theoretical
framework for analyzing the expressive power of GNNs to capture different graph
structures. Our results characterize the discriminative power of popular GNN
variants, such as Graph Convolutional Networks and GraphSAGE, and show that
they cannot learn to distinguish certain simple graph structures. We then
develop a simple architecture that is provably the most expressive among the
class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism
test. We empirically validate our theoretical findings on a number of graph
classification benchmarks, and demonstrate that our model achieves
state-of-the-art performance.
| true | true |
Xu, Keyulu and Hu, Weihua and Leskovec, Jure and Jegelka, Stefanie
| 2,018 | null | null | null |
arXiv:1810.00826
|
How Powerful are Graph Neural Networks?
|
How Powerful are Graph Neural Networks?
|
http://arxiv.org/pdf/1810.00826v3
|
Graph Neural Networks (GNNs) are an effective framework for representation
learning of graphs. GNNs follow a neighborhood aggregation scheme, where the
representation vector of a node is computed by recursively aggregating and
transforming representation vectors of its neighboring nodes. Many GNN variants
have been proposed and have achieved state-of-the-art results on both node and
graph classification tasks. However, despite GNNs revolutionizing graph
representation learning, there is limited understanding of their
representational properties and limitations. Here, we present a theoretical
framework for analyzing the expressive power of GNNs to capture different graph
structures. Our results characterize the discriminative power of popular GNN
variants, such as Graph Convolutional Networks and GraphSAGE, and show that
they cannot learn to distinguish certain simple graph structures. We then
develop a simple architecture that is provably the most expressive among the
class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism
test. We empirically validate our theoretical findings on a number of graph
classification benchmarks, and demonstrate that our model achieves
state-of-the-art performance.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
defferrardConvolutionalNeuralNetworks2016
|
\cite{defferrardConvolutionalNeuralNetworks2016}
|
Convolutional neural networks on graphs with fast localized spectral filtering
| null | null | true | false |
Defferrard, Micha{\"e}l and Bresson, Xavier and Vandergheynst, Pierre
| 2,016 | null | null | null | null |
Convolutional neural networks on graphs with fast localized spectral filtering
|
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
|
http://arxiv.org/pdf/1606.09375v3
|
In this work, we are interested in generalizing convolutional neural networks
(CNNs) from low-dimensional regular grids, where image, video and speech are
represented, to high-dimensional irregular domains, such as social networks,
brain connectomes or words' embedding, represented by graphs. We present a
formulation of CNNs in the context of spectral graph theory, which provides the
necessary mathematical background and efficient numerical schemes to design
fast localized convolutional filters on graphs. Importantly, the proposed
technique offers the same linear computational complexity and constant learning
complexity as classical CNNs, while being universal to any graph structure.
Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep
learning system to learn local, stationary, and compositional features on
graphs.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
chienAdaptiveUniversalGeneralized2021
|
\cite{chienAdaptiveUniversalGeneralized2021}
|
Adaptive Universal Generalized PageRank Graph Neural Network
|
http://arxiv.org/abs/2006.07988v6
|
In many important graph data processing applications the acquired information
includes both node features and observations of the graph topology. Graph
neural networks (GNNs) are designed to exploit both sources of evidence but
they do not optimally trade-off their utility and integrate them in a manner
that is also universal. Here, universality refers to independence on homophily
or heterophily graph assumptions. We address these issues by introducing a new
Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR
weights so as to jointly optimize node feature and topological information
extraction, regardless of the extent to which the node labels are homophilic or
heterophilic. Learned GPR weights automatically adjust to the node label
pattern, irrelevant on the type of initialization, and thereby guarantee
excellent learning performance for label patterns that are usually hard to
handle. Furthermore, they allow one to avoid feature over-smoothing, a process
which renders feature information nondiscriminative, without requiring the
network to be shallow. Our accompanying theoretical analysis of the GPR-GNN
method is facilitated by novel synthetic benchmark datasets generated by the
so-called contextual stochastic block model. We also compare the performance of
our GNN architecture with that of several state-of-the-art GNNs on the problem
of node-classification, using well-known benchmark homophilic and heterophilic
datasets. The results demonstrate that GPR-GNN offers significant performance
improvement compared to existing techniques on both synthetic and benchmark
data.
| true | true |
Chien, Eli and Peng, Jianhao and Li, Pan and Milenkovic, Olgica
| 2,020 | null | null | null |
arXiv:2006.07988
|
Adaptive Universal Generalized PageRank Graph Neural Network
|
Adaptive Universal Generalized PageRank Graph Neural Network
|
http://arxiv.org/pdf/2006.07988v6
|
In many important graph data processing applications the acquired information
includes both node features and observations of the graph topology. Graph
neural networks (GNNs) are designed to exploit both sources of evidence but
they do not optimally trade-off their utility and integrate them in a manner
that is also universal. Here, universality refers to independence on homophily
or heterophily graph assumptions. We address these issues by introducing a new
Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR
weights so as to jointly optimize node feature and topological information
extraction, regardless of the extent to which the node labels are homophilic or
heterophilic. Learned GPR weights automatically adjust to the node label
pattern, irrelevant on the type of initialization, and thereby guarantee
excellent learning performance for label patterns that are usually hard to
handle. Furthermore, they allow one to avoid feature over-smoothing, a process
which renders feature information nondiscriminative, without requiring the
network to be shallow. Our accompanying theoretical analysis of the GPR-GNN
method is facilitated by novel synthetic benchmark datasets generated by the
so-called contextual stochastic block model. We also compare the performance of
our GNN architecture with that of several state-of-the-art GNNs on the problem
of node-classification, using well-known benchmark homophilic and heterophilic
datasets. The results demonstrate that GPR-GNN offers significant performance
improvement compared to existing techniques on both synthetic and benchmark
data.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
heBernNetLearningArbitrary2021
|
\cite{heBernNetLearningArbitrary2021}
|
Bern{N}et: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
| null | null | true | false |
He, Mingguo and Wei, Zhewei and Huang, zengfeng and Xu, Hongteng
| 2,021 | null | null | null | null |
Bern{N}et: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
|
[PDF] Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
|
https://proceedings.neurips.cc/paper/2021/file/76f1cfd7754a6e4fc3281bcccb3d0902-Paper.pdf
|
BernNet is a graph neural network that learns arbitrary graph spectral filters using Bernstein polynomial approximation, designing spectral properties by
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
chenRevisitingGraphBased2020
|
\cite{chenRevisitingGraphBased2020}
|
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph
Convolutional Network Approach
|
http://arxiv.org/abs/2001.10167v1
|
Graph Convolutional Networks (GCNs) are state-of-the-art graph based
representation learning models by iteratively stacking multiple layers of
convolution aggregation operations and non-linear activation operations.
Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by
treating the user-item interaction behavior as a bipartite graph, some
researchers model higher-layer collaborative signals with GCNs. These GCN based
recommender models show superior performance compared to traditional works.
However, these models suffer from training difficulty with non-linear
activations for large user-item graphs. Besides, most GCN based models could
not model deeper layers due to the over smoothing effect with the graph
convolution operation. In this paper, we revisit GCN based CF models from two
aspects. First, we empirically show that removing non-linearities would enhance
recommendation performance, which is consistent with the theories in simple
graph convolutional networks. Second, we propose a residual network structure
that is specifically designed for CF with user-item interaction modeling, which
alleviates the over smoothing problem in graph convolution aggregation
operation with sparse user-item interaction data. The proposed model is a
linear model and it is easy to train, scale to large datasets, and yield better
efficiency and effectiveness on two real datasets. We publish the source code
at https://github.com/newlei/LRGCCF.
| true | true |
Chen, Lei and Wu, Le and Hong, Richang and Zhang, Kun and Wang, Meng
| 2,020 | null | null | null | null |
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph
Convolutional Network Approach
|
Revisiting Graph Based Collaborative Filtering: A Linear Residual ...
|
https://ojs.aaai.org/index.php/AAAI/article/view/5330
|
In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
wangNeuralGraphCollaborative2019
|
\cite{wangNeuralGraphCollaborative2019}
|
Neural Graph Collaborative Filtering
|
http://arxiv.org/abs/1905.08108v2
|
Learning vector representations (aka. embeddings) of users and items lies at
the core of modern recommender systems. Ranging from early matrix factorization
to recently emerged deep learning based methods, existing efforts typically
obtain a user's (or an item's) embedding by mapping from pre-existing features
that describe the user (or the item), such as ID and attributes. We argue that
an inherent drawback of such methods is that, the collaborative signal, which
is latent in user-item interactions, is not encoded in the embedding process.
As such, the resultant embeddings may not be sufficient to capture the
collaborative filtering effect.
In this work, we propose to integrate the user-item interactions -- more
specifically the bipartite graph structure -- into the embedding process. We
develop a new recommendation framework Neural Graph Collaborative Filtering
(NGCF), which exploits the user-item graph structure by propagating embeddings
on it. This leads to the expressive modeling of high-order connectivity in
user-item graph, effectively injecting the collaborative signal into the
embedding process in an explicit manner. We conduct extensive experiments on
three public benchmarks, demonstrating significant improvements over several
state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further
analysis verifies the importance of embedding propagation for learning better
user and item representations, justifying the rationality and effectiveness of
NGCF. Codes are available at
https://github.com/xiangwang1223/neural_graph_collaborative_filtering.
| true | true |
Wang, Xiang and He, Xiangnan and Wang, Meng and Feng, Fuli and Chua, Tat-Seng
| 2,019 | null | null | null | null |
Neural Graph Collaborative Filtering
|
Neural Graph Collaborative Filtering
|
http://arxiv.org/pdf/1905.08108v2
|
Learning vector representations (aka. embeddings) of users and items lies at
the core of modern recommender systems. Ranging from early matrix factorization
to recently emerged deep learning based methods, existing efforts typically
obtain a user's (or an item's) embedding by mapping from pre-existing features
that describe the user (or the item), such as ID and attributes. We argue that
an inherent drawback of such methods is that, the collaborative signal, which
is latent in user-item interactions, is not encoded in the embedding process.
As such, the resultant embeddings may not be sufficient to capture the
collaborative filtering effect.
In this work, we propose to integrate the user-item interactions -- more
specifically the bipartite graph structure -- into the embedding process. We
develop a new recommendation framework Neural Graph Collaborative Filtering
(NGCF), which exploits the user-item graph structure by propagating embeddings
on it. This leads to the expressive modeling of high-order connectivity in
user-item graph, effectively injecting the collaborative signal into the
embedding process in an explicit manner. We conduct extensive experiments on
three public benchmarks, demonstrating significant improvements over several
state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further
analysis verifies the importance of embedding propagation for learning better
user and item representations, justifying the rationality and effectiveness of
NGCF. Codes are available at
https://github.com/xiangwang1223/neural_graph_collaborative_filtering.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
heLightGCNSimplifyingPowering2020
|
\cite{heLightGCNSimplifyingPowering2020}
|
Light{GCN}: Simplifying and Powering Graph Convolution Network for Recommendation
| null | null | true | false |
He, Xiangnan and Deng, Kuan and Wang, Xiang and Li, Yan and Zhang, YongDong and Wang, Meng
| 2,020 | null | null | null | null |
Light{GCN}: Simplifying and Powering Graph Convolution Network for Recommendation
|
[PDF] LightGCN: Simplifying and Powering Graph Convolution Network for ...
|
https://arxiv.org/pdf/2002.02126
|
In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. We propose a new model named
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
maoUltraGCNUltraSimplification2021
|
\cite{maoUltraGCNUltraSimplification2021}
|
Ultra{GCN}: Ultra Simplification of Graph Convolutional Networks for Recommendation
| null | null | true | false |
Mao, Kelong and Zhu, Jieming and Xiao, Xi and Lu, Biao and Wang, Zhaowei and He, Xiuqiang
| 2,021 | null | null | null | null |
Ultra{GCN}: Ultra Simplification of Graph Convolutional Networks for Recommendation
|
UltraGCN: Ultra Simplification of Graph Convolutional Networks for ...
|
https://arxiv.org/abs/2110.15114
|
View a PDF of the paper titled UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation, by Kelong Mao and 5 other authors In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation. View a PDF of the paper titled UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation, by Kelong Mao and 5 other authors - [x] Bibliographic Explorer Toggle - [x] Connected Papers Toggle - [x] Litmaps Toggle - [x] alphaXiv Toggle - [x] Links to Code Toggle - [x] DagsHub Toggle - [x] Links to Code Toggle - [x] ScienceCast Toggle - [x] Replicate Toggle - [x] Core recommender toggle
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
heSGCF2023
|
\cite{heSGCF2023}
|
Simplifying graph-based collaborative filtering for recommendation
| null | null | true | false |
He, Li and Wang, Xianzhi and Wang, Dingxian and Zou, Haoyuan and Yin, Hongzhi and Xu, Guandong
| 2,023 | null | null | null | null |
Simplifying graph-based collaborative filtering for recommendation
|
Simplifying Graph-based Collaborative Filtering for ...
|
https://opus.lib.uts.edu.au/bitstream/10453/164889/4/Simplifying%20Graph-based%20Collaborative%20Filtering%20for%20Recommendation.pdf
|
by L He · 2023 · Cited by 28 — First, we remove non-linearities to en- hance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second,
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
sunNeighborInteractionAware2020
|
\cite{sunNeighborInteractionAware2020}
|
Neighbor Interaction Aware Graph Convolution Networks for Recommendation
| null | null | true | false |
Sun, Jianing and Zhang, Yingxue and Guo, Wei and Guo, Huifeng and Tang, Ruiming and He, Xiuqiang and Ma, Chen and Coates, Mark
| 2,020 | null | null | null | null |
Neighbor Interaction Aware Graph Convolution Networks for Recommendation
|
Neighbor Interaction Aware Graph Convolution Networks ...
|
https://dl.acm.org/doi/10.1145/3397271.3401123
|
Neighbor Interaction Aware Graph Convolution Networks for Recommendation | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval * Hotjar 3Learn more about this providerImage 8**_hjSession_#**Collects statistics on the visitor's visits to the website, such as the number of visits, average time spent on the website and what pages have been read.**Maximum Storage Duration**: 1 day**Type**: HTTP Cookie **_hjSessionUser_#**Collects statistics on the visitor's visits to the website, such as the number of visits, average time spent on the website and what pages have been read.**Maximum Storage Duration**: 1 year**Type**: HTTP Cookie **_hjTLDTest**Registers statistical data on users' behaviour on the website. * Zhou Z Yan X(2025)Multi-modal Recommendation based on Graph Neural Networks 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT)10.1109/ISCAIT64916.2025.11010536(219-223)Online publication date: 21-Mar-2025https://doi.org/10.1109/ISCAIT64916.2025.11010536 * Zhang M Liao X Wang X Wang X Jin L(2025)Multi-neighbor social recommendation with attentional graph convolutional network Data Mining and Knowledge Discovery 10.1007/s10618-025-01094-7**39**:3 Online publication date: 20-Mar-2025https://doi.org/10.1007/s10618-025-01094-7
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
wangDisentangledGraphCollaborative2020
|
\cite{wangDisentangledGraphCollaborative2020}
|
Disentangled Graph Collaborative Filtering
|
http://arxiv.org/abs/2007.01764v1
|
Learning informative representations of users and items from the interaction
data is of crucial importance to collaborative filtering (CF). Present
embedding functions exploit user-item relationships to enrich the
representations, evolving from a single user-item instance to the holistic
interaction graph. Nevertheless, they largely model the relationships in a
uniform manner, while neglecting the diversity of user intents on adopting the
items, which could be to pass time, for interest, or shopping for others like
families. Such uniform approach to model user interests easily results in
suboptimal representations, failing to model diverse relationships and
disentangle user intents in representations.
In this work, we pay special attention to user-item relationships at the
finer granularity of user intents. We hence devise a new model, Disentangled
Graph Collaborative Filtering (DGCF), to disentangle these factors and yield
disentangled representations. Specifically, by modeling a distribution over
intents for each user-item interaction, we iteratively refine the intent-aware
interaction graphs and representations. Meanwhile, we encourage independence of
different intents. This leads to disentangled representations, effectively
distilling information pertinent to each intent. We conduct extensive
experiments on three benchmark datasets, and DGCF achieves significant
improvements over several state-of-the-art models like NGCF, DisenGCN, and
MacridVAE. Further analyses offer insights into the advantages of DGCF on the
disentanglement of user intents and interpretability of representations. Our
codes are available in
https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering.
| true | true |
Wang, Xiang and Jin, Hongye and Zhang, An and He, Xiangnan and Xu, Tong and Chua, Tat-Seng
| 2,020 | null | null | null | null |
Disentangled Graph Collaborative Filtering
|
Disentangled Graph Collaborative Filtering
|
http://arxiv.org/pdf/2007.01764v1
|
Learning informative representations of users and items from the interaction
data is of crucial importance to collaborative filtering (CF). Present
embedding functions exploit user-item relationships to enrich the
representations, evolving from a single user-item instance to the holistic
interaction graph. Nevertheless, they largely model the relationships in a
uniform manner, while neglecting the diversity of user intents on adopting the
items, which could be to pass time, for interest, or shopping for others like
families. Such uniform approach to model user interests easily results in
suboptimal representations, failing to model diverse relationships and
disentangle user intents in representations.
In this work, we pay special attention to user-item relationships at the
finer granularity of user intents. We hence devise a new model, Disentangled
Graph Collaborative Filtering (DGCF), to disentangle these factors and yield
disentangled representations. Specifically, by modeling a distribution over
intents for each user-item interaction, we iteratively refine the intent-aware
interaction graphs and representations. Meanwhile, we encourage independence of
different intents. This leads to disentangled representations, effectively
distilling information pertinent to each intent. We conduct extensive
experiments on three benchmark datasets, and DGCF achieves significant
improvements over several state-of-the-art models like NGCF, DisenGCN, and
MacridVAE. Further analyses offer insights into the advantages of DGCF on the
disentanglement of user intents and interpretability of representations. Our
codes are available in
https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
liuInterestawareMessagePassingGCN2021
|
\cite{liuInterestawareMessagePassingGCN2021}
|
Interest-aware Message-Passing GCN for Recommendation
|
http://arxiv.org/abs/2102.10044v2
|
Graph Convolution Networks (GCNs) manifest great potential in recommendation.
This is attributed to their capability on learning good user and item
embeddings by exploiting the collaborative signals from the high-order
neighbors. Like other GCN models, the GCN based recommendation models also
suffer from the notorious over-smoothing problem - when stacking more layers,
node embeddings become more similar and eventually indistinguishable, resulted
in performance degradation. The recently proposed LightGCN and LR-GCN alleviate
this problem to some extent, however, we argue that they overlook an important
factor for the over-smoothing problem in recommendation, that is, high-order
neighboring users with no common interests of a user can be also involved in
the user's embedding learning in the graph convolution operation. As a result,
the multi-layer graph convolution will make users with dissimilar interests
have similar embeddings. In this paper, we propose a novel Interest-aware
Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order
graph convolution inside subgraphs. The subgraph consists of users with similar
interests and their interacted items. To form the subgraphs, we design an
unsupervised subgraph generation module, which can effectively identify users
with common interests by exploiting both user feature and graph structure. To
this end, our model can avoid propagating negative information from high-order
neighbors into embedding learning. Experimental results on three large-scale
benchmark datasets show that our model can gain performance improvement by
stacking more layers and outperform the state-of-the-art GCN-based
recommendation models significantly.
| true | true |
Liu, Fan and Cheng, Zhiyong and Zhu, Lei and Gao, Zan and Nie, Liqiang
| 2,021 | null | null | null | null |
Interest-aware Message-Passing GCN for Recommendation
|
Interest-aware Message-Passing GCN for Recommendation
|
https://dl.acm.org/doi/10.1145/3442381.3449986
|
In this paper, we propose a novel Interest-aware Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order graph convolution inside
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
kongLinearNonLinearThat2022
|
\cite{kongLinearNonLinearThat2022}
|
Linear, or Non-Linear, That is the Question!
|
http://arxiv.org/abs/2111.07265v2
|
There were fierce debates on whether the non-linear embedding propagation of
GCNs is appropriate to GCN-based recommender systems. It was recently found
that the linear embedding propagation shows better accuracy than the non-linear
embedding propagation. Since this phenomenon was discovered especially in
recommender systems, it is required that we carefully analyze the linearity and
non-linearity issue. In this work, therefore, we revisit the issues of i) which
of the linear or non-linear propagation is better and ii) which factors of
users/items decide the linearity/non-linearity of the embedding propagation. We
propose a novel Hybrid Method of Linear and non-linEar collaborative filTering
method (HMLET, pronounced as Hamlet). In our design, there exist both linear
and non-linear propagation steps, when processing each user or item node, and
our gating module chooses one of them, which results in a hybrid model of the
linear and non-linear GCN-based collaborative filtering (CF). The proposed
model yields the best accuracy in three public benchmark datasets. Moreover, we
classify users/items into the following three classes depending on our gating
modules' selections: Full-Non-Linearity (FNL), Partial-Non-Linearity (PNL), and
Full-Linearity (FL). We found that there exist strong correlations between
nodes' centrality and their class membership, i.e., important user/item nodes
exhibit more preferences towards the non-linearity during the propagation
steps. To our knowledge, we are the first who design a hybrid method and report
the correlation between the graph centrality and the linearity/non-linearity of
nodes. All HMLET codes and datasets are available at:
https://github.com/qbxlvnf11/HMLET.
| true | true |
Kong, Taeyong and Kim, Taeri and Jeon, Jinsung and Choi, Jeongwhan and Lee, Yeon-Chang and Park, Noseong and Kim, Sang-Wook
| 2,022 | null | null | null | null |
Linear, or Non-Linear, That is the Question!
|
[2111.07265] Linear, or Non-Linear, That is the Question! - arXiv
|
https://arxiv.org/abs/2111.07265
|
It was recently found that the linear embedding propagation shows better accuracy than the non-linear embedding propagation.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
fanGraphTrendFiltering2022
|
\cite{fanGraphTrendFiltering2022}
|
Graph Trend Filtering Networks for Recommendations
|
http://arxiv.org/abs/2108.05552v2
|
Recommender systems aim to provide personalized services to users and are
playing an increasingly important role in our daily lives. The key of
recommender systems is to predict how likely users will interact with items
based on their historical online behaviors, e.g., clicks, add-to-cart,
purchases, etc. To exploit these user-item interactions, there are increasing
efforts on considering the user-item interactions as a user-item bipartite
graph and then performing information propagation in the graph via Graph Neural
Networks (GNNs). Given the power of GNNs in graph representation learning,
these GNNs-based recommendation methods have remarkably boosted the
recommendation performance. Despite their success, most existing GNNs-based
recommender systems overlook the existence of interactions caused by unreliable
behaviors (e.g., random/bait clicks) and uniformly treat all the interactions,
which can lead to sub-optimal and unstable performance. In this paper, we
investigate the drawbacks (e.g., non-adaptive propagation and non-robustness)
of existing GNN-based recommendation methods. To address these drawbacks, we
introduce a principled graph trend collaborative filtering method and propose
the Graph Trend Filtering Networks for recommendations (GTN) that can capture
the adaptive reliability of the interactions. Comprehensive experiments and
ablation studies are presented to verify and understand the effectiveness of
the proposed framework. Our implementation based on PyTorch is available at
https://github.com/wenqifan03/GTN-SIGIR2022.
| true | true |
Fan, Wenqi and Liu, Xiaorui and Jin, Wei and Zhao, Xiangyu and Tang, Jiliang and Li, Qing
| 2,022 | null | null | null | null |
Graph Trend Filtering Networks for Recommendations
|
Graph Trend Filtering Networks for Recommendations
|
http://arxiv.org/pdf/2108.05552v2
|
Recommender systems aim to provide personalized services to users and are
playing an increasingly important role in our daily lives. The key of
recommender systems is to predict how likely users will interact with items
based on their historical online behaviors, e.g., clicks, add-to-cart,
purchases, etc. To exploit these user-item interactions, there are increasing
efforts on considering the user-item interactions as a user-item bipartite
graph and then performing information propagation in the graph via Graph Neural
Networks (GNNs). Given the power of GNNs in graph representation learning,
these GNNs-based recommendation methods have remarkably boosted the
recommendation performance. Despite their success, most existing GNNs-based
recommender systems overlook the existence of interactions caused by unreliable
behaviors (e.g., random/bait clicks) and uniformly treat all the interactions,
which can lead to sub-optimal and unstable performance. In this paper, we
investigate the drawbacks (e.g., non-adaptive propagation and non-robustness)
of existing GNN-based recommendation methods. To address these drawbacks, we
introduce a principled graph trend collaborative filtering method and propose
the Graph Trend Filtering Networks for recommendations (GTN) that can capture
the adaptive reliability of the interactions. Comprehensive experiments and
ablation studies are presented to verify and understand the effectiveness of
the proposed framework. Our implementation based on PyTorch is available at
https://github.com/wenqifan03/GTN-SIGIR2022.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
guoJGCF2023
|
\cite{guoJGCF2023}
|
On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based
Graph Collaborative Filtering
|
http://arxiv.org/abs/2306.03624v1
|
Collaborative filtering (CF) is an important research direction in
recommender systems that aims to make recommendations given the information on
user-item interactions. Graph CF has attracted more and more attention in
recent years due to its effectiveness in leveraging high-order information in
the user-item bipartite graph for better recommendations. Specifically, recent
studies show the success of graph neural networks (GNN) for CF is attributed to
its low-pass filtering effects. However, current researches lack a study of how
different signal components contributes to recommendations, and how to design
strategies to properly use them well. To this end, from the view of spectral
transformation, we analyze the important factors that a graph filter should
consider to achieve better performance. Based on the discoveries, we design
JGCF, an efficient and effective method for CF based on Jacobi polynomial bases
and frequency decomposition strategies. Extensive experiments on four widely
used public datasets show the effectiveness and efficiency of the proposed
methods, which brings at most 27.06% performance gain on Alibaba-iFashion.
Besides, the experimental results also show that JGCF is better at handling
sparse datasets, which shows potential in making recommendations for cold-start
users.
| true | true |
Guo, Jiayan and Du, Lun and Chen, Xu and Ma, Xiaojun and Fu, Qiang and Han, Shi and Zhang, Dongmei and Zhang, Yan
| 2,023 | null | null | null | null |
On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based
Graph Collaborative Filtering
|
A Jacobi Polynomial-based Graph Collaborative Filtering
|
https://www.bohrium.com/paper-details/on-manipulating-signals-of-user-item-graph-a-jacobi-polynomial-based-graph-collaborative-filtering/873226422896820882-108611
|
On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering ... 2025-06-16. ACM Transactions on
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
wangCollaborationAwareGraphConvolutional2023
|
\cite{wangCollaborationAwareGraphConvolutional2023}
|
Collaboration-Aware Graph Convolutional Network for Recommender Systems
|
http://arxiv.org/abs/2207.06221v4
|
Graph Neural Networks (GNNs) have been successfully adopted in recommender
systems by virtue of the message-passing that implicitly captures collaborative
effect. Nevertheless, most of the existing message-passing mechanisms for
recommendation are directly inherited from GNNs without scrutinizing whether
the captured collaborative effect would benefit the prediction of user
preferences. In this paper, we first analyze how message-passing captures the
collaborative effect and propose a recommendation-oriented topological metric,
Common Interacted Ratio (CIR), which measures the level of interaction between
a specific neighbor of a node with the rest of its neighbors. After
demonstrating the benefits of leveraging collaborations from neighbors with
higher CIR, we propose a recommendation-tailored GNN, Collaboration-Aware Graph
Convolutional Network (CAGCN), that goes beyond 1-Weisfeiler-Lehman(1-WL) test
in distinguishing non-bipartite-subgraph-isomorphic graphs. Experiments on six
benchmark datasets show that the best CAGCN variant outperforms the most
representative GNN-based recommendation model, LightGCN, by nearly 10% in
Recall@20 and also achieves around 80% speedup. Our code is publicly available
at https://github.com/YuWVandy/CAGCN.
| true | true |
Wang, Yu and Zhao, Yuying and Zhang, Yi and Derr, Tyler
| 2,023 | null | null | null | null |
Collaboration-Aware Graph Convolutional Network for Recommender Systems
|
Collaboration-Aware Graph Convolutional Network for ...
|
https://dl.acm.org/doi/abs/10.1145/3543507.3583229
|
by Y Wang · 2023 · Cited by 70 — We propose a recommendation-tailored GNN, Collaboration-Aware Graph Convolutional Network (CAGCN), that goes beyond 1-Weisfeiler-Lehman(1-WL) test.
|
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
|
2505.00552v1
|
zhuGiffCF2024
|
\cite{zhuGiffCF2024}
|
Graph Signal Diffusion Model for Collaborative Filtering
|
http://arxiv.org/abs/2311.08744v3
|
Collaborative filtering is a critical technique in recommender systems. It
has been increasingly viewed as a conditional generative task for user feedback
data, where newly developed diffusion model shows great potential. However,
existing studies on diffusion model lack effective solutions for modeling
implicit feedback. Particularly, the standard isotropic diffusion process
overlooks correlation between items, misaligned with the graphical structure of
the interaction space. Meanwhile, Gaussian noise destroys personalized
information in a user's interaction vector, causing difficulty in its
reconstruction. In this paper, we adapt standard diffusion model and propose a
novel Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF).
To better represent the correlated distribution of user-item interactions, we
define a generalized diffusion process using heat equation on the item-item
similarity graph. Our forward process smooths interaction signals with an
advanced family of graph filters, introducing the graph adjacency as beneficial
prior knowledge for recommendation. Our reverse process iteratively refines and
sharpens latent signals in a noise-free manner, where the updates are
conditioned on the user's history and computed from a carefully designed
two-stage denoiser, leading to high-quality reconstruction. Finally, through
extensive experiments, we show that GiffCF effectively leverages the advantages
of both diffusion model and graph signal processing, and achieves
state-of-the-art performance on three benchmark datasets.
| true | true |
Zhu, Yunqin and Wang, Chao and Zhang, Qi and Xiong, Hui
| 2,024 | null | null | null | null |
Graph Signal Diffusion Model for Collaborative Filtering
|
Graph Signal Diffusion Model for Collaborative Filtering
|
http://arxiv.org/pdf/2311.08744v3
|
Collaborative filtering is a critical technique in recommender systems. It
has been increasingly viewed as a conditional generative task for user feedback
data, where newly developed diffusion model shows great potential. However,
existing studies on diffusion model lack effective solutions for modeling
implicit feedback. Particularly, the standard isotropic diffusion process
overlooks correlation between items, misaligned with the graphical structure of
the interaction space. Meanwhile, Gaussian noise destroys personalized
information in a user's interaction vector, causing difficulty in its
reconstruction. In this paper, we adapt standard diffusion model and propose a
novel Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF).
To better represent the correlated distribution of user-item interactions, we
define a generalized diffusion process using heat equation on the item-item
similarity graph. Our forward process smooths interaction signals with an
advanced family of graph filters, introducing the graph adjacency as beneficial
prior knowledge for recommendation. Our reverse process iteratively refines and
sharpens latent signals in a noise-free manner, where the updates are
conditioned on the user's history and computed from a carefully designed
two-stage denoiser, leading to high-quality reconstruction. Finally, through
extensive experiments, we show that GiffCF effectively leverages the advantages
of both diffusion model and graph signal processing, and achieves
state-of-the-art performance on three benchmark datasets.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.