mlsanityData to 14 Oct 2026

Large Language Models for Generative Recommendation: A Survey and Visionary Discussions

arXiv 2309.01157 ↗First posted 3 Sep 2023cs.IR cs.AI cs.CLcited by 73

Not on the map. Why?

Abstract (arXiv)

Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e.g., recommender systems (RS). However, most of the related work treats an LLM as a component of the conventional recommendation pipeline (e.g., as a feature extractor), which may not be able to fully leverage the generative power of LLM. Instead of separating the recommendation process into multiple stages, such as score computation and re-ranking, this process can be simplified to one stage with LLM: directly generating recommendations from the complete pool of items. This survey reviews the progress, methods, and future directions of LLM-based generative recommendation by examining three questions: 1) What generative recommendation is, 2) Why RS should advance to generative recommendation, and 3) How to implement LLM-based generative recommendation for various RS tasks. We hope that this survey can provide the context and guidance needed to explore this interesting and emerging topic.

Key facts. Cited by 73 papers in the map (data through 14 Oct 2026). Builds on 62 of its 113 references that are in the map. First posted on arXiv 3 Sep 2023.

Builds on

62 of 113 references.

References · 62

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  4. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  5. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  6. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  7. 2021SoundStream: An End-to-End Neural Audio Codeccited by 771
  8. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 658
  9. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  10. 2023Augmented Language Models: a Surveycited by 403
  11. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
  12. 2023A Survey on Large Language Models for Recommendationcited by 379
38 more
  1. 2023AudioPaLM: A Large Language Model That Can Speak and Listencited by 343
  2. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 337
  3. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  4. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  5. 2023Language is All a Graph Needscited by 220
  6. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  7. 2023OpenAGI: When LLM Meets Domain Expertscited by 197
  8. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
  9. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  10. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  11. 2023Uncovering ChatGPT's Capabilities in Recommender Systemscited by 161
  12. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  13. 2020A Survey on Conversational Recommender Systemscited by 143
  14. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  15. 2023User Behavior Simulation with Large Language Model based Agentscited by 140
  16. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 131
  17. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  18. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 127
  19. 2018Conversational Recommender Systemcited by 115
  20. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 114
  21. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  22. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 103
  23. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  24. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
  25. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  26. 2022Personalized Prompt Learning for Explainable Recommendationcited by 79
  27. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  28. 2023A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systemscited by 73
  29. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  30. 2023GenRec: Large Language Model for Generative Recommendationcited by 63
  31. 2021Personalized Transformer for Explainable Recommendationcited by 59
  32. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  33. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Modelscited by 48
  34. 2023VIP5: Towards Multimodal Foundation Models for Recommendationcited by 46
  35. 2023Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systemscited by 45
  36. 2023Exploring Large Language Model for Graph Data Understanding in Online Job Recommendationscited by 45
  37. 2023Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insightscited by 38
  38. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38

50 most cited of 62 All 62 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 26; 2025: 16; 2026: 27.

73 papers.

Sort:

By area:

  1. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 634
  2. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  3. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 98
  4. 2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
  5. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  6. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 53
  7. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  8. 2023Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestioncited by 43
  9. 2024EVOLvE: Evaluating and Optimizing LLMs For In-Context Explorationcited by 34
  10. 2025Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducementscited by 32
  11. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 28
  12. 2024How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendationcited by 27
38 more
  1. 2024Large Language Model Distilling Medication Recommendation Modelcited by 26
  2. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  3. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  4. 2024CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracercited by 17
  5. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  6. 2024Federated Recommendation via Hybrid Retrieval Augmented Generationcited by 16
  7. 2024The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Modelscited by 15
  8. 2024A Survey of Generative Search and Recommendation in the Era of Large Language Modelscited by 15
  9. 2025The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systemscited by 14
  10. 2024A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systemscited by 13
  11. 2025RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systemscited by 12
  12. 2023FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Predictioncited by 11
  13. 2025LettinGo: Explore User Profile Generation for Recommendation Systemcited by 11
  14. 2024Large Language Model Enhanced Recommender Systems: A Surveycited by 10
  15. 2024Comprehending Knowledge Graphs with Large Language Models for Recommender Systemscited by 9
  16. 2023LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendationcited by 8
  17. 2024STAR: A Simple Training-free Approach for Recommendations using Large Language Modelscited by 8
  18. 2025GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusioncited by 7
  19. 2024Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systemscited by 6
  20. 2025Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendationcited by 6
  21. 2024PAP-REC: Personalized Automatic Prompt for Recommendation Language Modelcited by 4
  22. 2025Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogscited by 4
  23. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 4
  24. 2025KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendationcited by 3
  25. 2025Multi-Aspect Cross-modal Quantization for Generative Recommendationcited by 3
  26. 2026ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendationcited by 3
  27. 2024Privacy in LLM-based Recommendation: Recent Advances and Future Directionscited by 2
  28. 2024Leveraging Inter-Chunk Interactions for Enhanced Retrieval in Large Language Model-Based Question Answeringcited by 2
  29. 2024Uncovering Biases with Reflective Large Language Modelscited by 2
  30. 2026Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?cited by 2
  31. 2026MuonRec: Shifting the Optimizer Paradigm Beyond Adam in Scalable Generative Recommendationcited by 2
  32. 2026IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshucited by 2
  33. 2026LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbotscited by 2
  34. 2025Brownian Bridge Diffusion for Sequential Recommendationcited by 1
  35. 2025Token-Controlled Re-ranking for Sequential Recommendation via LLMscited by 1
  36. 2026Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoderscited by 1
  37. 2026UniGRec: Unified Generative Recommendation with Soft Identifiers for End-to-End Optimizationcited by 1
  38. 2026MATRAG: Multi-Agent Transparent Retrieval-Augmented Generation for Explainable Recommendationscited by 1

50 most cited of 73 All 73 (JSON)