LLM4Rec-Awesome-Papers
A list of awesome papers and resources of recommender system on large language model (LLM).
https://github.com/WLiK/LLM4Rec-Awesome-Papers
Last synced: 4 days ago
JSON representation
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The papers and related projects
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No Tuning
- Are GPT Embeddings Useful for Ads and Recommendation? - Peng/GPT4SM) | GPT |
- 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 |
- 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 |
- 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 |
- 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 |
- 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 |
- 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 |
- Learning vector-quantized item representation for transferable sequential recommenders - rec) | BERT |
- 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 |
- 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 |
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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 |
- 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 |
- CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation - 7B & Qwen2-1.5 |
- Text-like Encoding of Collaborative Information in Large Language Models for Recommendation - 7B |
- 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
- Prompt Distillation for Efficient LLM-based Recommendation
- 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
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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
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Related Tutorial
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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
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