mlsanityData to 14 Oct 2026

Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond

arXiv 2410.19744 ↗First posted 10 Oct 2024cs.IR cs.AIcited by 26

On the map: LLM reasoning → LLM reasoning for recommendation

Abstract (arXiv)

Large language models (LLMs) have not only revolutionized the field of natural language processing (NLP) but also have the potential to bring a paradigm shift in many other fields due to their remarkable abilities of language understanding, as well as impressive generalization capabilities and reasoning skills. As a result, recent studies have actively attempted to harness the power of LLMs to improve recommender systems, and it is imperative to thoroughly review the recent advances and challenges of LLM-based recommender systems. Unlike existing work, this survey does not merely analyze the classifications of LLM-based recommendation systems according to the technical framework of LLMs. Instead, it investigates how LLMs can better serve recommendation tasks from the perspective of the recommender system community, thus enhancing the integration of large language models into the research of recommender system and its practical application. In addition, the long-standing gap between academic research and industrial applications related to recommender systems has not been well discussed, especially in the era of large language models. In this review, we introduce a novel taxonomy that originates from the intrinsic essence of recommendation, delving into the application of large language model-based recommendation systems and their industrial implementation. Specifically, we propose a three-tier structure that more accurately reflects the developmental progression of recommendation systems from research to practical implementation, including representing and understanding, scheming and utilizing, and industrial deployment. Furthermore, we discuss critical challenges and opportunities in this emerging field. A more up-to-date version of the papers is maintained at: https://github.com/jindongli-Ai/Next-Generation-LLM-based-Recommender-Systems-Survey.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning. Builds on 142 of its 173 references that are in the map. First posted on arXiv 10 Oct 2024.

Builds on

142 of 173 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation12 Aug
  2. 2024Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation7 Aug
  3. 2024LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation3 Jul
  4. 2024CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation11 Mar
  5. 2024Rethinking Large Language Model Architectures for Sequential Recommendations14 Feb
  6. 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation18 Dec

Other references · 136

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  6. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,711
  7. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,040
  8. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
  9. 2019Neural Graph Collaborative Filteringcited by 661
  10. 2023Instruction Tuning for Large Language Models: A Surveycited by 623
  11. 2023A Survey on Large Language Models for Recommendationcited by 379
  12. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 337
38 more
  1. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  2. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  4. 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendationscited by 188
  5. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendationcited by 172
  6. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  7. 2023Uncovering ChatGPT's Capabilities in Recommender Systemscited by 161
  8. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  9. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  10. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 131
  11. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  12. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 127
  13. 2024Data-efficient Fine-tuning for LLM-based Recommendationcited by 104
  14. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 103
  15. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  16. 2023CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendationcited by 100
  17. 2023Leveraging Large Language Models for Sequential Recommendationcited by 78
  18. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  19. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  20. 2023Towards Open-World Recommendation with Knowledge Augmentation from Large Language Modelscited by 74
  21. 2023A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systemscited by 73
  22. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  23. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  24. 2023Collaborative Large Language Model for Recommender Systemscited by 64
  25. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  26. 2023GenRec: Large Language Model for Generative Recommendationcited by 63
  27. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  28. 2023LLaRA: Large Language-Recommendation Assistantcited by 56
  29. 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Modelscited by 54
  30. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  31. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 53
  32. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 53
  33. 2020DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendationcited by 46
  34. 2023Exploring Large Language Model for Graph Data Understanding in Online Job Recommendationscited by 45
  35. 2019Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systemscited by 41
  36. 2024Large Language Models for Next Point-of-Interest Recommendationcited by 40
  37. 2023E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendationcited by 39
  38. 2024Harnessing Large Language Models for Text-Rich Sequential Recommendationcited by 38

50 most cited of 136 All 136 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 11; 2026: 13.

26 papers.

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  1. 2025A Survey of Personalized Large Language Models: Progress and Future Directionscited by 71
  2. 2025Implicit Reasoning in Large Language Models: A Comprehensive Surveycited by 43
  3. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 33
  4. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systemscited by 23
  5. 2024SPRec: Self-Play to Debias LLM-based Recommendationcited by 18
  6. 2024APEX: An Extensible and Dynamism-Aware Simulator for Automated Parallel Execution in LLM Servingcited by 15
  7. 2026PersonaVLM: Long-Term Personalized Multimodal LLMscited by 11
  8. 2025Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspectivecited by 6
  9. 2025CTRL-Rec: Controlling Recommender Systems With Natural Languagecited by 4
  10. 2025Diffusion Models in Recommendation Systems: A Surveycited by 3
  11. 2025Research on Low-Latency Inference and Training Efficiency Optimization for Graph Neural Network and Large Language Model-Based Recommendation Systemscited by 2
  12. 2026Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?cited by 2
14 more
  1. 2026AdNanny: One Reasoning LLM for All Offline Ads Recommendation Taskscited by 1
  2. 2026Learning Variable-Length Tokenization for Generative Recommendationcited by 1
  3. 2025TransLLM: A Unified Multi-Task Foundation Framework for Urban Transportation via Learnable Promptingcited by 0
  4. 2025RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedbackcited by 0
  5. 2025Shielded RecRL: Explanation Generation for Recommender Systems without Ranking Degradationcited by 0
  6. 2026Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendationcited by 0
  7. 2026SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systemscited by 0
  8. 2026Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMscited by 0
  9. 2026AgenticRec: A Recommendation-Oriented Agentic Framework with Progressive Tool-Integrated Reasoning Optimizationcited by 0
  10. 2026Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillationcited by 0
  11. 2026Rethinking Semantic Collaborative Integration: Why Alignment Is Not Enoughcited by 0
  12. 2026Rethinking Fairness in LLM-Based Recommender Systems: A Surveycited by 0
  13. 2026rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendationcited by 0
  14. 2026RPCBench: A Benchmark for Proactive Premise Critique in LLM-based Recommendationcited by 0

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  1. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
  2. 2024All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
  3. 2025Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
  4. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  5. 2025GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
  6. 2023Empowering Few-Shot Recommender Systems with Large Language Models -- Enhanced Representations
  7. 2023PALR: Personalization Aware LLMs for Recommendation
  8. 2024Large Language Models Enhanced Collaborative Filtering