mlsanityData to 7 Oct 2026

Large Language Model Enhanced Recommender Systems: A Survey

arXiv 2412.13432 ↗First posted 18 Dec 2024cs.IR cs.AIcited by 10

Not on the map. Why?

Abstract (arXiv)

Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently, the integration of LLM into RS, known as LLM-Enhanced Recommender Systems (LLMERS), has garnered significant interest due to its potential to address latency and memory constraints in real-world applications. This paper presents a comprehensive survey of the latest research efforts aimed at leveraging LLM to enhance RS capabilities. We identify a critical shift in the field with the move towards incorporating LLM into the online system, notably by avoiding their use during inference. Our survey categorizes the existing LLMERS approaches into three primary types based on the component of the RS model being augmented: Knowledge Enhancement, Interaction Enhancement, and Model Enhancement. We provide an in-depth analysis of each category, discussing the methodologies, challenges, and contributions of recent studies. Furthermore, we highlight several promising research directions that could further advance the field of LLMERS.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 49 of its 83 references that are in the map. First posted on arXiv 18 Dec 2024.

Builds on

49 of 83 references.

References · 49

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  3. 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
  4. 2023A Survey on Large Language Models for Recommendationcited by 378
  5. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 241
  6. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  7. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendationcited by 171
  8. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  9. 2023A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systemscited by 73
  10. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  11. 2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
  12. 2018Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender Systemcited by 60
37 more
  1. 2023Exploring Large Language Model for Graph Data Understanding in Online Job Recommendationscited by 45
  2. 2024NoteLLM-2: Multimodal Large Representation Models for Recommendationcited by 44
  3. 2022Multi-Modal Cross-Domain Alignment Network for Video Moment Retrievalcited by 43
  4. 2024NoteLLM: A Retrievable Large Language Model for Note Recommendationcited by 33
  5. 2022AutoField: Automating Feature Selection in Deep Recommender Systemscited by 30
  6. 2024Large Language Models Enhanced Collaborative Filteringcited by 27
  7. 2024Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregationcited by 26
  8. 2024QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishoucited by 26
  9. 2024Large Language Model Distilling Medication Recommendation Modelcited by 25
  10. 2024Can Small Language Models be Good Reasoners for Sequential Recommendation?cited by 25
  11. 2024MMREC: LLM Based Multi-Modal Recommender Systemcited by 22
  12. 2024LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Applicationcited by 21
  13. 2023Embedding in Recommender Systems: A Surveycited by 19
  14. 2024LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendationcited by 19
  15. 2024Large Language Models as Data Augmenters for Cold-Start Item Recommendationcited by 19
  16. 2024LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendationcited by 19
  17. 2024Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graphcited by 16
  18. 2023FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Predictioncited by 11
  19. 2024DynLLM: When Large Language Models Meet Dynamic Graph Recommendationcited by 11
  20. 2024DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender Systemcited by 11
  21. 2024Large Language Model with Graph Convolution for Recommendationcited by 10
  22. 2024LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendationcited by 10
  23. 2024Comprehending Knowledge Graphs with Large Language Models for Recommender Systemscited by 9
  24. 2024LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understandingcited by 6
  25. 2024X-Reflect: Cross-Reflection Prompting for Multimodal Recommendationcited by 6
  26. 2024Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graphcited by 5
  27. 2024EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendationscited by 4
  28. 2024FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancementcited by 4
  29. 2024A Survey on Data-Centric Recommender Systemscited by 3
  30. 2024Large Language Models Make Sample-Efficient Recommender Systemscited by 3
  31. 2024All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Eracited by 3
  32. 2024HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendationcited by 3
  33. 2024Large Language Model Enhanced Hard Sample Identification for Denoising Recommendationcited by 3
  34. 2024GPRec: Bi-level User Modeling for Deep Recommenderscited by 3
  35. 2024Harnessing Large Language Models for Group POI Recommendationscited by 3
  36. 2024CELA: Cost-Efficient Language Model Alignment for CTR Predictioncited by 2
  37. 2024LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 5.

10 papers.

  1. 2025A Survey of Personalized Large Language Models: Progress and Future Directionscited by 65
  2. 2024LLM-Powered User Simulator for Recommender Systemcited by 38
  3. 2025Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimizationcited by 8
  4. 2025Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendationscited by 2
  5. 2025Language Models as Semantic Augmenters for Sequential Recommenderscited by 2
  6. 2026Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curationcited by 1
  7. 2026MATRAG: Multi-Agent Transparent Retrieval-Augmented Generation for Explainable Recommendationscited by 1
  8. 2026HELM: A Human-Centered Evaluation Framework for LLM-Powered Recommender Systemscited by 0
  9. 2026rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendationcited by 0
  10. 2026AtomRec: Evolving Atomic Memory for Agentic Recommendationcited by 0