The papers and related projects

• No Tuning

  • LLM4Vis: Explainable Visualization Recommendation using ChatGPT
  • LLMRec: Large Language Models with Graph Augmentation for Recommendation
  • Representation Learning with Large Language Models for Recommendation - 3.5 |
  • Knowledge Prompt-tuning for Sequential Recommendation - 3.5 |
  • Large Language Model Can Interpret Latent Space of Sequential Recommender - 7b |
  • Large Language Models as Zero-Shot Conversational Recommenders - as-zero-shot-conversational-recsys) | GPT-3.5-turbo ,GPT-4,BAIZE,Vicuna |
  • On Generative Agents in Recommendation
  • Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging - 2+GPT4 |
  • Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Enhancing Recommender Systems with Large Language Model Reasoning Graphs - 3.5/GPT4|
  • RAH! RecSys-Assistant-Human: A Human-Central Recommendation Framework with Large Language Models
  • LLM-Rec: Personalized Recommendation via Prompting Large Language Models - 3 |
  • Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata
  • Retrieval-augmented Recommender System: Enhancing Recommender Systems with Large Language Models
  • LLM Based Generation of Item-Description for Recommendation System
  • Large Language Models are Competitive Near Cold-start Recommenders for Language-and Item-based Preferences
  • Large Language Model Augmented Narrative Driven Recommendations
  • Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
  • RecAgent: A Novel Simulation Paradigm for Recommender Systems - GSAI/YuLan-Rec)| ChatGPT|
  • AnyPredict: Foundation Model for Tabular Prediction
  • Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models - crs) | ChatGPT |
  • Large Language Models are Zero-Shot Rankers for Recommender Systems
  • Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation - zhang/FaiRLLM) | ChatGPT |
  • A First Look at LLM-Powered Generative News Recommendation - requests) | ChatGPT |
  • Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT
  • Uncovering ChatGPT's Capabilities in Recommender Systems
  • Is ChatGPT a Good Recommender? A Preliminary Study
  • Learning vector-quantized item representation for transferable sequential recommenders - rec) | BERT |
  • Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent
  • Generative Recommendation: Towards Next-generation Recommender Paradigm
  • Zero-Shot Next-Item Recommendation using Large Pretrained Language Models - Edgerunners/LLM-Next-Item-Rec) | GPT-3.5 |
  • Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
  • Zero-Shot Recommendation as Language Modeling - FGaLGdo5rPzxf3vemKllbh2esT?usp=sharing) | GPT-2 |
  • Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning - 2/ DialoGPT /BART |
  • LLMRec: Large Language Models with Graph Augmentation for Recommendation
  • K-LaMP: Knowledge-Guided Language Model Pre-training for Sequential Recommendation - 4 |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • LLM Based Generation of Item-Description for Recommendation System
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Large Language Models as Data Augmenters for Cold-Start Item Recommendation
  • Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models - V, LLaVA2 |
  • LLMRec: Large Language Models with Graph Augmentation for Recommendation
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Knowledge Prompt-tuning for Sequential Recommendation - 3.5 |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • On Generative Agents in Recommendation
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • K-LaMP: Knowledge-Guided Language Model Pre-training for Sequential Recommendation - 4 |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Large Language Models as Data Augmenters for Cold-Start Item Recommendation
  • Rethinking Large Language Model Architectures for Sequential Recommendations
  • Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models - V, LLaVA2 |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • RecAgent: A Novel Simulation Paradigm for Recommender Systems - GSAI/YuLan-Rec)| ChatGPT|
  • Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models - crs) | ChatGPT |
  • Large Language Models are Zero-Shot Rankers for Recommender Systems
  • Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation - zhang/FaiRLLM) | ChatGPT |
  • Uncovering ChatGPT's Capabilities in Recommender Systems
  • Is ChatGPT a Good Recommender? A Preliminary Study
  • Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent
  • Zero-Shot Next-Item Recommendation using Large Pretrained Language Models - Edgerunners/LLM-Next-Item-Rec) | GPT-3.5 |
  • Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
  • Zero-Shot Recommendation as Language Modeling - FGaLGdo5rPzxf3vemKllbh2esT?usp=sharing) | GPT-2 |
  • Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning - 2/ DialoGPT /BART |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • A First Look at LLM-Powered Generative News Recommendation - requests) | ChatGPT |
  • Generative Recommendation: Towards Next-generation Recommender Paradigm
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Rethinking Large Language Model Architectures for Sequential Recommendations
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Retrieval-augmented Recommender System: Enhancing Recommender Systems with Large Language Models
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
  • LLM-Rec: Personalized Recommendation via Prompting Large Language Models - 3 |
  • Learning vector-quantized item representation for transferable sequential recommenders - rec) | BERT |
  • Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |

• Supervised Fine-Tuning

  • RecMind: Large Language Model Powered Agent For Recommendation
  • LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking - 2 |
  • Collaborative Large Language Model for Recommender Systems
  • A Multi-facet Paradigm to Bridge Large Language Model and Recommendation - large and LLaMA-7B |
  • Leveraging Large Language Models for Pre-trained Recommender Systems - 10B|
  • Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM - 6B,P5 |
  • Prompt Distillation for Efficient LLM-based Recommendation
  • A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
  • A Large Language Model Enhanced Conversational Recommender System - T5/LLaMA|
  • Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
  • Generative Job Recommendations with Large Language Model
  • Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
  • GenRec: Large Language Model for Generative Recommendation
  • Leveraging Large Language Models in Conversational Recommender Systems
  • ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
  • Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models
  • PBNR: Prompt-based News Recommender System
  • Generative Sequential Recommendation with GPTRec - IR@SIGIR | 2023 | N/A | GPT-2 |
  • CTRL: Connect Tabular and Language Model for CTR Prediction
  • UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation - silverbullet/UniTRec) | BART|
  • Large Language Models for User Interest Journeys
  • Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
  • Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
  • PALR: Personalization Aware LLMs for Recommendation
  • Recommendation as instruction following: A large language model empowered recommendation approach - T5-3B |
  • Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
  • Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study
  • TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation - convolutional-matrix-completion) | Llama-7B |
  • GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation - 2 |
  • Where to go next for recommender systems? id-vs. modality-based recommender models revisited - repl/IDvs.MoRec)| BERT|
  • Language models are realistic tabular data generators - 2|
  • M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
  • Towards understanding and mitigating unintended biases in language model-driven conversational recommendation - Bias-LMRec) | BERT |
  • Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
  • Personalized prompt learning for explainable recommendation - 2 |
  • Language models as recommender systems: Evaluations and limitations - 2 |
  • SPAR: Personalized Content-Based Recommendation via Long Engagement Attention
  • Large Language Model Interaction Simulator for Cold-Start Item Recommendation
  • LLM-based Federated Recommendation
  • E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation - 2 |
  • LLaRA: Aligning Large Language Models with Sequential Recommenders - 2 |
  • Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM - 6B,P5 |
  • Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
  • LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations - wang/LLM4REC.git) | GPT2 |
  • Enhancing Recommendation Diversity by Re-ranking with Large Language Models
  • Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
  • Large Language Model with Graph Convolution for Recommendation
  • Large Language Model Interaction Simulator for Cold-Start Item Recommendation
  • LLM-based Federated Recommendation
  • SPAR: Personalized Content-Based Recommendation via Long Engagement Attention
  • Aligning Large Language Models for Controllable Recommendations
  • Can Small Language Models be Good Reasoners for Sequential Recommendation?
  • Federated Recommendation via Hybrid Retrieval Augmented Generation
  • NoteLLM: A Retrievable Large Language Model for Note Recommendation
  • Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
  • PALR: Personalization Aware LLMs for Recommendation
  • Recommendation as instruction following: A large language model empowered recommendation approach - T5-3B |
  • TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation - convolutional-matrix-completion) | Llama-7B |
  • M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
  • Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
  • Personalized prompt learning for explainable recommendation - 2 |
  • LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations - wang/LLM4REC.git) | GPT2 |
  • Enhancing Recommendation Diversity by Re-ranking with Large Language Models
  • Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
  • Large Language Model with Graph Convolution for Recommendation
  • GenRec: Large Language Model for Generative Recommendation

• Related Survey

  • Large Language Models for Generative Recommendation: A Survey and Visionary Discussions
  • Robust Recommender System: A Survey and Future Directions
  • A Survey on Multi-Behavior Sequential Recommendation
  • When large language models meet personalization: Perspectives of challenges and opportunities
  • Recommender systems in the era of large language models (llms)
  • A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News
  • How Can Recommender Systems Benefit from Large Language Models: A Survey
  • Pre-train, prompt and recommendation: A comprehensive survey of language modelling paradigm adaptations in recommender systems
  • Pre-train, prompt and recommendation: A comprehensive survey of language modelling paradigm adaptations in recommender systems

• Related Tutorial

  • Large Language Models for Recommendation: Progresses and Future Directions - AP | 2023|
  • Tutorial on Large Language Models for Recommendation
  • Large Language Models for Recommendation: Progresses and Future Directions - AP | 2023|
  • Tutorial on Large Language Models for Recommendation

• Common Datasets

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• Single card (RTX 3090) debuggable generative language models that support Chinese corpus

  • baichuan-7B
  • THUDM - 6B](https://github.com/THUDM/ChatGLM-6B)** | 2023 |
  • FreedomIntelligence
  • bloomz-7b1
  • LianjiaTech