mlsanityData to 14 Oct 2026

How Can Recommender Systems Benefit from Large Language Models: A Survey

arXiv 2306.05817 ↗First posted 9 Jun 2023cs.IR cs.AIcited by 142

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Abstract (arXiv)

With the rapid development of online services, recommender systems (RS) have become increasingly indispensable for mitigating information overload. Despite remarkable progress, conventional recommendation models (CRM) still have some limitations, e.g., lacking open-world knowledge, and difficulties in comprehending users' underlying preferences and motivations. Meanwhile, large language models (LLM) have shown impressive general intelligence and human-like capabilities, which mainly stem from their extensive open-world knowledge, reasoning ability, as well as their comprehension of human culture and society. Consequently, the emergence of LLM is inspiring the design of recommender systems and pointing out a promising research direction, i.e., whether we can incorporate LLM and benefit from their knowledge and capabilities to compensate for the limitations of CRM. In this paper, we conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems. Specifically, we summarize existing works from two orthogonal aspects: where and how to adapt LLM to RS. For the WHERE question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, user interaction, and pipeline controller. For the HOW question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLM or not, and whether to involve conventional recommendation models for inference. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We actively maintain a GitHub repository for papers and other related resources: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys/.

Key facts. Cited by 142 papers in the map (data through 14 Oct 2026). Builds on 122 of its 271 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

122 of 271 references.

References · 122

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 21; 2024: 46; 2025: 49; 2026: 26.

142 papers.

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  1. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 634
  2. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  3. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  4. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  5. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  6. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 98
  7. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  8. 2025A Survey of AI Agent Protocolscited by 81
  9. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  10. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  11. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  12. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
38 more
  1. 2024CoIR: A Comprehensive Benchmark for Code Information Retrieval Modelscited by 60
  2. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  3. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  4. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 33
  5. 2025Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systemscited by 32
  6. 2023ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Predictioncited by 27
  7. 2024Can Small Language Models be Good Reasoners for Sequential Recommendation?cited by 26
  8. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  9. 2025CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agentcited by 25
  10. 2024CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendationcited by 24
  11. 2024LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Applicationcited by 21
  12. 2024Agentic Information Retrievalcited by 21
  13. 2024A Survey on Diffusion Models for Recommender Systemscited by 20
  14. 2024Enhancing Recommendation Diversity by Re-ranking with Large Language Modelscited by 19
  15. 2024Stealthy Attack on Large Language Model based Recommendationcited by 19
  16. 2024Prospect Personalized Recommendation on Large Language Model-based Agent Platformcited by 19
  17. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  18. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  19. 2024Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendationcited by 17
  20. 2024Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITAcited by 17
  21. 2024Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendationcited by 17
  22. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  23. 2024Towards Efficient and Effective Unlearning of Large Language Models for Recommendationcited by 16
  24. 2023Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendationscited by 15
  25. 2023CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Modelscited by 15
  26. 2024The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Modelscited by 15
  27. 2025Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendationcited by 15
  28. 2025InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generationcited by 15
  29. 2023Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challengescited by 13
  30. 2025Position: The Real Barrier to LLM Agent Usability is Agentic ROIcited by 12
  31. 2023FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Predictioncited by 11
  32. 2025Bias Beware: The Impact of Cognitive Biases on LLM-Driven Product Recommendationscited by 11
  33. 2025Slow Thinking for Sequential Recommendationcited by 11
  34. 2023MAP: A Model-agnostic Pretraining Framework for Click-through Rate Predictioncited by 10
  35. 2024Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendationcited by 10
  36. 2024SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Modelcited by 10
  37. 2024Large Language Model Enhanced Recommender Systems: A Surveycited by 10
  38. 2024Parameter-Efficient Conversational Recommender System as a Language Processing Taskcited by 9

50 most cited of 142 All 142 (JSON)

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