{'fraction_non_alphanumeric': 0.02050113895216401, 'fraction_numerical': 0.0, 'mean_word_length': 5.146853146853147, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Neural Factorization for Offer Recommendation using Knowledge Graph Embeddings

Companies send a lot of promotional offers and coupons to customers to attract them to buy more. Offer recommendation systems can help to identify relevant offers to users. In this paper, we present a Neural Factorization (NF) model for the task of Offer recommendation. We represent users and offers with Knowledge Graph Embeddings (KGE). Specifically, we model the available data in the form of a Knowledge Graph (KG) and learn embeddings for entities and relations using a standard KGE technique called TransE. We also incorporate the user temporal features in the NF model using Long Short Term Memory (LSTM) with attention framework. We experiment with Kaggle Acquire Valued Shoppers Challenge dataset and show that the performance of our model is significantly better than tree-based methods.