mlsanityData to 14 Oct 2026

Recommender Systems in the Era of Large Language Models (LLMs)

arXiv 2307.02046 ↗First posted 5 Jul 2023cs.IR cs.AI cs.CLcited by 189

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Cited by 22 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an important component of our daily life, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have made significant advancements in enhancing recommender systems by modeling user-item interactions and incorporating textual side information, DNN-based methods still face limitations, such as difficulties in understanding users' interests and capturing textual side information, inabilities in generalizing to various recommendation scenarios and reasoning on their predictions, etc. Meanwhile, the emergence of Large Language Models (LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), due to their remarkable abilities in fundamental responsibilities of language understanding and generation, as well as impressive generalization and reasoning capabilities. As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems. Given the rapid evolution of this research direction in recommender systems, there is a pressing need for a systematic overview that summarizes existing LLM-empowered recommender systems, to provide researchers in relevant fields with an in-depth understanding. Therefore, in this paper, we conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting. More specifically, we first introduce representative methods to harness the power of LLMs (as a feature encoder) for learning representations of users and items. Then, we review recent techniques of LLMs for enhancing recommender systems from three paradigms, namely pre-training, fine-tuning, and prompting. Finally, we comprehensively discuss future directions in this emerging field.

Key facts. Cited by 189 papers in the map (data through 14 Oct 2026). Builds on 111 of its 170 references that are in the map. First posted on arXiv 5 Jul 2023.

Builds on

111 of 170 references.

References · 111

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38 more
  1. 2023A Survey of Large Language Modelscited by 2,576
  2. 2020Extracting Training Data from Large Language Modelscited by 2,189
  3. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  4. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
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  6. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
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  9. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
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  13. 2019Graph Neural Networks for Social Recommendationcited by 714
  14. 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
  15. 2021Learning To Retrieve Prompts for In-Context Learningcited by 648
  16. 2019Unified Vision-Language Pre-Training for Image Captioning and VQAcited by 494
  17. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  18. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
  19. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
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  21. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 337
  22. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  23. 2023FinGPT: Open-Source Financial Large Language Modelscited by 253
  24. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  25. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 240
  26. 2023HuatuoGPT, towards Taming Language Model to Be a Doctorcited by 214
  27. 2023Recommender Systems with Generative Retrievalcited by 198
  28. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
  29. 2021Reinforcement learning based recommender systems: A surveycited by 188
  30. 2023Sources of Hallucination by Large Language Models on Inference Taskscited by 169
  31. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  32. 2023Uncovering ChatGPT's Capabilities in Recommender Systemscited by 161
  33. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  34. 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
  35. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  36. 2023DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Taskcited by 131
  37. 2023Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisitedcited by 124
  38. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 121

50 most cited of 111 All 111 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 25; 2024: 65; 2025: 65; 2026: 34.

189 papers.

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  1. 2023A Survey on Evaluation of Large Language Modelscited by 1,242
  2. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  3. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  4. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  5. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
  6. 2023Knowledge Editing for Large Language Models: A Surveycited by 194
  7. 2023Large Language Models for Generative Information Extraction: A Surveycited by 185
  8. 2023ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generationcited by 168
  9. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  10. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  11. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  12. 2023When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applicationscited by 111
38 more
  1. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 103
  2. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 98
  3. 2024CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologiescited by 98
  4. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  5. 2024A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trendscited by 84
  6. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  7. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  8. 2024HYDRA: Model Factorization Framework for Black-Box LLM Personalizationcited by 60
  9. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  10. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendationcited by 57
  11. 2023A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challengecited by 55
  12. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  13. 2023Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestioncited by 43
  14. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  15. 2023E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendationcited by 39
  16. 2024A Survey of Mambacited by 39
  17. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  18. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  19. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 33
  20. 2024BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batchingcited by 32
  21. 2024LLMs are Also Effective Embedding Models: An In-depth Overviewcited by 32
  22. 2024Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challengescited by 30
  23. 2024APEER: Automatic Prompt Engineering Enhances Large Language Model Rerankingcited by 28
  24. 2024Rethinking Large Language Model Architectures for Sequential Recommendationscited by 27
  25. 2024Large Language Model Distilling Medication Recommendation Modelcited by 26
  26. 2024Can Small Language Models be Good Reasoners for Sequential Recommendation?cited by 26
  27. 2024Large Language Models as Recommender Systems: A Study of Popularity Biascited by 26
  28. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  29. 2024Optimal Design for Reward Modeling in RLHFcited by 25
  30. 2025CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agentcited by 25
  31. 2024Auctions with LLM Summariescited by 24
  32. 2024CulturePark: Boosting Cross-cultural Understanding in Large Language Modelscited by 24
  33. 2023Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphscited by 23
  34. 2023MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansioncited by 23
  35. 2024LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Applicationcited by 21
  36. 2024Agentic Information Retrievalcited by 21
  37. 2024Enhancing Recommendation Diversity by Re-ranking with Large Language Modelscited by 19
  38. 2024Prospect Personalized Recommendation on Large Language Model-based Agent Platformcited by 19

50 most cited of 189 All 189 (JSON)

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  4. 2025GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
  5. 2025Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
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