mlsanityData to 14 Oct 2026

A Survey on Large Language Models for Recommendation

arXiv 2305.19860 ↗First posted 31 May 2023cs.IR cs.AIcited by 379

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Cited by 47 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers on LLMs for recommendation, https://github.com/WLiK/LLM4Rec.

Key facts. Cited by 379 papers in the map (data through 14 Oct 2026). Builds on 57 of its 153 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

57 of 153 references.

References · 57

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  6. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  7. 2023A Survey of Large Language Modelscited by 2,576
  8. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  9. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  10. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 520
  11. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
  12. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 360
38 more
  1. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 337
  2. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  3. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  4. 2023Recommender Systems with Generative Retrievalcited by 198
  5. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
  6. 2021Towards Continual Knowledge Learning of Language Modelscited by 174
  7. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  8. 2023Uncovering ChatGPT's Capabilities in Recommender Systemscited by 161
  9. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  10. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 148
  11. 2023Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisitedcited by 124
  12. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 121
  13. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 116
  14. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 114
  15. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  16. 2016Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbotscited by 103
  17. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 103
  18. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
  19. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 86
  20. 2022Personalized Prompt Learning for Explainable Recommendationcited by 79
  21. 2023Generative Recommendation: Towards Next-generation Recommender Paradigmcited by 70
  22. 2021Empowering News Recommendation with Pre-trained Language Modelscited by 63
  23. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  24. 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Modelscited by 54
  25. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  26. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Modelscited by 48
  27. 2023Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systemscited by 45
  28. 2023Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insightscited by 38
  29. 2020What does BERT know about books, movies and music? Probing BERT for Conversational Recommendationcited by 36
  30. 2022Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systemscited by 31
  31. 2023Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insightscited by 29
  32. 2021Zero-Shot Recommendation as Language Modelingcited by 28
  33. 2021Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Informationcited by 26
  34. 2023Prompt Learning for News Recommendationcited by 25
  35. 2023UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendationcited by 20
  36. 2021Training Large-Scale News Recommenders with Pretrained Language Models in the Loopcited by 18
  37. 2021Tiny-NewsRec: Effective and Efficient PLM-based News Recommendationcited by 16
  38. 2023PBNR: Prompt-based News Recommender Systemcited by 14

50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 32; 2024: 101; 2025: 142; 2026: 104.

379 papers.

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  1. 2023Prompt Cache: Modular Attention Reuse for Low-Latency Inferencecited by 268
  2. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 212
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  4. 2023Large Language Models for Generative Information Extraction: A Surveycited by 185
  5. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  6. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  7. 2024A Survey on Large Language Model Acceleration based on KV Cache Managementcited by 131
  8. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  9. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 98
  10. 2024PQCache: Product Quantization-based KVCache for Long Context LLM Inferencecited by 95
  11. 2024The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Modelscited by 93
  12. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
38 more
  1. 2024Evolutionary Computation in the Era of Large Language Model: Survey and Roadmapcited by 90
  2. 2025A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Modelscited by 87
  3. 2024FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"cited by 86
  4. 2025MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agentscited by 84
  5. 2024User-LLM: Efficient LLM Contextualization with User Embeddingscited by 76
  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. 2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
  9. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  10. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  11. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 53
  12. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  13. 2024Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillationcited by 47
  14. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  15. 2023Exploring Large Language Model for Graph Data Understanding in Online Job Recommendationscited by 45
  16. 2023Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestioncited by 43
  17. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  18. 2023Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Modelscited by 41
  19. 2023E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendationcited by 39
  20. 2023Disentangled Representation Learning with Large Language Models for Text-Attributed Graphscited by 36
  21. 2024Do LLM Agents Have Regret? A Case Study in Online Learning and Gamescited by 35
  22. 2024Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendationcited by 33
  23. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 33
  24. 2024Customizing Language Models with Instance-wise LoRA for Sequential Recommendationcited by 32
  25. 2023A Large Language Model Enhanced Conversational Recommender Systemcited by 31
  26. 2023RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agentscited by 30
  27. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 28
  28. 2023ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Predictioncited by 27
  29. 2025G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendationcited by 27
  30. 2025A Survey on Personalized and Pluralistic Preference Alignment in Large Language Modelscited by 27
  31. 2024Large Language Model Distilling Medication Recommendation Modelcited by 26
  32. 2024Can Small Language Models be Good Reasoners for Sequential Recommendation?cited by 26
  33. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  34. 2024LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendationscited by 24
  35. 2024CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendationcited by 24
  36. 2025Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generationcited by 24
  37. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systemscited by 23
  38. 2024Review-driven Personalized Preference Reasoning with Large Language Models for Recommendationcited by 22

50 most cited of 379 All 379 (JSON)