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Large Language Model-enhanced Recommender System Papers

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Awesome-LLM4RS-Papers

This is a paper list about Large Language Model-enhanced Recommender System. It also contains some related works.

Keywords: recommendation system, large language models

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Paper List

  • Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System, arxiv 2023, [paper].
  • GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation, arxiv 2023, [paper].
  • Generative Recommendation: Towards Next-generation Recommender Paradigm, arxiv 2023, [paper].
  • Is ChatGPT a Good Recommender? A Preliminary Study, arxiv 2023, [paper].
  • TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation, arxiv 2023, [paper], [code].
  • Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models, arxiv 2023, [paper].
  • Uncovering ChatGPT's Capabilities in Recommender Systems, arxiv 2023, [paper],[code].
  • Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach, arxiv 2023, [paper].
  • A First Look at LLM-Powered Generative News Recommendation, arxiv 2023, [paper].
  • Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT, arxiv 2023, [paper].
  • Zero-Shot Next-Item Recommendation using Large Pretrained Language Models, arxiv 2023, [paper], [code].
  • Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction, arxiv 2023, [paper].
  • Large Language Models are Zero-Shot Rankers for Recommender Systems, arxiv 2023, [paper], [code].
  • Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation, arxiv 2023, [paper], [code].
  • Leveraging Large Language Models in Conversational Recommender Systems, arxiv 2023, [paper].
  • Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models, arxiv 2023, [paper], [code].
  • PALR: Personalization Aware LLMs for Recommendation, arxiv 2023, [paper].
  • Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations, arxiv 2023, [paper].
  • A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News, arxiv 2023, [paper].
  • Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models, arxiv 2023, [paper].

Survey

  • A Survey on Large Language Models for Recommendation, arxiv 2023, [paper].
  • How Can Recommender Systems Benefit from Large Language Models: A Survey, arxiv 2023, [paper].

Universal Representation Learning

Github Repository "Universal_user_representations for recommendation" [link].

  • ID-Agnostic User Behavior Pre-training for Sequential Recommendation, CCIR 2022, [paper].
  • Towards Universal Sequence Representation Learning for Recommender Systems, KDD 2022, [paper],[code].
  • Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders, WWW 2023, [paper],[code].
  • One4all User Representation for Recommender Systems in E-commerce, arvix 2021, [paper].
  • Text Is All You Need: Learning Language Representations for Sequential Recommendation, KDD 2023, [paper].

Generative Retrieval

  • Recommender Systems with Generative Retrieval, arvix 2023, [paper].
  • Generative Sequential Recommendation with GPTRec, SIGIR 2023 workshop, [paper].

Pretrain Language Model and Prompt Learing

Survey paper: Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems, arxiv 2023, [paper].

  • Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5), arvix 2022, [paper],[code].
  • Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective, SIGIR 2022, [paper].
  • M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems, arvix 2022, [paper].
  • Personalized Prompt for Sequential Recommendation, arvix 2022, [paper].

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