Giter VIP home page Giter VIP logo

awesome-llm4rs-papers's Introduction

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

Welcome to open an issue or make a pull request!

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].
  • Recommender Systems in the Era of Large Language Models (LLMs), arxiv 2023, [paper].

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].
  • TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation, RecSys 2023 Short Paper, [paper], [code].
  • Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models, arxiv 2023, [paper].
  • 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].
  • 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].
  • Large Language Model for Generative Recommendation, arxiv 2023, [paper].
  • GenRec: Large Language Model for Generative Recommendation, arxiv 2023, [paper].
  • Generative Job Recommendations with Large Language Model, arxiv 2023, [paper].
  • Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations, arxiv 2023, [paper].
  • LLM-Rec: Personalized Recommendation via Prompting Large Language Models, arxiv 2023, [paper].
  • A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems, arxiv 2023, [paper].
  • LLMRec: Benchmarking Large Language Models on Recommendation Task, arxiv 2023, [paper],[code].
  • Prompt Distillation for Efficient LLM-based Recommendation, CIKM 2023, [paper], [code].
  • Large Language Models as Zero-Shot Conversational Recommenders, CIKM 2023, [paper], [code].
  • Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation, arxiv 2023, [paper].
  • Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging, arxiv 2023, [paper].
  • LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking, arxiv 2023, [paper], [code].
  • Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences, Recsys 2023, [paper].
  • CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation, arxiv 2023, [paper].
  • Large Language Model Augmented Narrative Driven Recommendations, RecSys 2023 Short Paper, [paper].
  • Leveraging Large Language Models for Sequential Recommendation, RecSys 2023 LBR, [paper], [code].
  • ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models, WSDM 2024, [paper], [code].
  • LLaRA: Aligning Large Language Models with Sequential Recommenders, arxiv 2023, [paper], [code].
  • LLM4Vis: Explainable Visualization Recommendation using ChatGPT, arxiv 2023, [paper], [code].
  • E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation, arxiv 2023, [paper], [code].
  • Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation, arxiv 2023, [paper], [code].

Agent4Rec

  • When Large Language Model based Agent Meets User Behavior Analysis: A Novel User Simulation Paradigm, arxiv 2023, [paper].
  • RecMind: Large Language Model Powered Agent For Recommendation, arxiv 2023, [paper].
  • On Generative Agents in Recommendation, arxiv 2023, [paper], [code].
  • AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems, arxiv 2023, [paper].

Knowledge Augmentation

  • Enhancing Recommender Systems with Large Language Model Reasoning Graphs, arxiv 2023, [paper].
  • Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models, arxiv 2023, [paper].
  • LLMRec: Large Language Models with Graph Augmentation for Recommendation, WSDM 2024, [paper], [code], [blog in Chinese].

Perspective

  • Language models as recommender systems: Evaluations and limitations, NeurIPS Workshop 2021, [paper].
  • Generative Recommendation: Towards Next-generation Recommender Paradigm, arxiv 2023, [paper].
  • Where to Go Next for Recommender Systems? ID- vs.Modality-based recommender models revisited, SIGIR 2023, [paper].[code]
  • Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights, arxiv 2023, [paper].
  • Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights, arxiv 2023, [paper].
  • Is ChatGPT a Good Recommender? A Preliminary Study, arxiv 2023, [paper].
  • Evaluating ChatGPT as a Recommender System: A Rigorous Approach, arxiv 2023, [paper].
  • Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences, RecSys 2023 Short Paper, [paper].
  • Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation, RecSys 2023 Short Paper, [paper], [code].
  • Uncovering ChatGPT's Capabilities in Recommender Systems, RecSys 2023 LBR, [paper],[code].

Universal Representation Learning

Github Repository: "Universal_user_representations for recommendation" [link].

  • Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation, SIGIR 2020, [paper]. [code]
  • One Person, One Model, One World: Learning Continual User Representation without Forgetting, SIGIR 2021, [paper].[code]
  • ID-Agnostic User Behavior Pre-training for Sequential Recommendation, CCIR 2022, [paper].
  • Towards Universal Sequence Representation Learning for Recommender Systems, KDD 2022, [paper],[code].
  • TransRec: learning transferable recommendation from mixture-of-modality feedback, arxiv 2022, [paper].
  • 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].
  • Collaborative Large Language Model for Recommender Systems, arvix 2023, [paper],[code].

Generative Retrieval

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

Pretrain Language Model and Prompt Learning

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].
  • Knowledge Prompt-tuning for Sequential Recommendation, ACM MM 2023, [paper], [code].

Dataset

  • Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation, arvix 2023, [paper], [KDD Cup 2023].
  • PixelRec: An Image Dataset for Benchmarking Recommender Systems with Raw Pixels, arvix 2023, [paper], [link].

awesome-llm4rs-papers's People

Contributors

nancheng58 avatar peijiesun avatar kaidf avatar hanbingwang2001 avatar furyton avatar ruyu-li avatar weiwei1206 avatar hyp1231 avatar hyc9 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.