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Ashish Patel

ashishpatel26

https://www.linkedin.com/in/ashishpatel2604/

AI & ML interests

GenerativeAI, LLMs, Quantum Computing, Computer Vision, Audio Engineering

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liked a Space 11 days ago

deepseek-ai/deepseek-vl2-small

reacted to singhsidhukuldeep's post with 🔥 about 1 month ago

Groundbreaking Survey on Large Language Models in Recommendation Systems! Just read a comprehensive survey that maps out how LLMs are revolutionizing recommender systems. The authors have meticulously categorized existing approaches into two major paradigms: Discriminative LLMs for Recommendation: - Leverages BERT-like models for understanding user-item interactions - Uses fine-tuning and prompt tuning to adapt pre-trained models - Excels at tasks like user representation learning and ranking Generative LLMs for Recommendation: - Employs GPT-style models to directly generate recommendations - Implements innovative techniques like in-context learning and zero-shot recommendation - Supports natural language interaction and explanation generation Key Technical Insights: - Novel taxonomy of modeling paradigms: LLM Embeddings + RS, LLM Tokens + RS, and LLM as RS - Integration methods spanning from simple prompting to sophisticated instruction tuning - Hybrid approaches combining collaborative filtering with LLM capabilities - Advanced prompt engineering techniques for controlled recommendation generation Critical Challenges Identified: - Position and popularity bias in LLM recommendations - Limited context length affecting user history processing - Need for better evaluation metrics for generative recommendations - Controlled output generation and personalization challenges This work opens exciting possibilities for next-gen recommendation systems while highlighting crucial areas for future research.

upvoted a collection about 1 month ago

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liked a Space 11 days ago

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Generate responses using images and text input

reacted to singhsidhukuldeep's post with 🔥 about 1 month ago

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Groundbreaking Survey on Large Language Models in Recommendation Systems!

Just read a comprehensive survey that maps out how LLMs are revolutionizing recommender systems. The authors have meticulously categorized existing approaches into two major paradigms:

Discriminative LLMs for Recommendation:
- Leverages BERT-like models for understanding user-item interactions
- Uses fine-tuning and prompt tuning to adapt pre-trained models
- Excels at tasks like user representation learning and ranking

Generative LLMs for Recommendation:
- Employs GPT-style models to directly generate recommendations
- Implements innovative techniques like in-context learning and zero-shot recommendation
- Supports natural language interaction and explanation generation

Key Technical Insights:
- Novel taxonomy of modeling paradigms: LLM Embeddings + RS, LLM Tokens + RS, and LLM as RS
- Integration methods spanning from simple prompting to sophisticated instruction tuning
- Hybrid approaches combining collaborative filtering with LLM capabilities
- Advanced prompt engineering techniques for controlled recommendation generation

Critical Challenges Identified:
- Position and popularity bias in LLM recommendations
- Limited context length affecting user history processing
- Need for better evaluation metrics for generative recommendations
- Controlled output generation and personalization challenges

This work opens exciting possibilities for next-gen recommendation systems while highlighting crucial areas for future research.