mlsanityData to 14 Oct 2026

Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review

arXiv 2402.18590 ↗First posted 11 Feb 2024cs.IR cs.AIcited by 12

On the map: LLM reasoning → LLM reasoning for recommendation

Abstract (arXiv)

The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional recommenders. Unlike conventional systems lacking direct user interaction data, LLMs exhibit exceptional proficiency in recommending items, showcasing their adeptness in comprehending intricacies of language. This marks a fundamental paradigm shift in the realm of recommendations. Amidst the dynamic research landscape, researchers actively harness the language comprehension and generation capabilities of LLMs to redefine the foundations of recommendation tasks. The investigation thoroughly explores the inherent strengths of LLMs within recommendation frameworks, encompassing nuanced contextual comprehension, seamless transitions across diverse domains, adoption of unified approaches, holistic learning strategies leveraging shared data reservoirs, transparent decision-making, and iterative improvements. Despite their transformative potential, challenges persist, including sensitivity to input prompts, occasional misinterpretations, and unforeseen recommendations, necessitating continuous refinement and evolution in LLM-driven recommender systems.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning. Builds on 67 of its 80 references that are in the map. First posted on arXiv 11 Feb 2024.

Builds on

67 of 80 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation18 Dec
  2. 2023Enhancing Recommender Systems with Large Language Model Reasoning Graphs21 Aug
  3. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models22 May

Other references · 64

  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. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  5. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  6. 2022A Survey on In-context Learningcited by 721
  7. 2023Instruction Tuning with GPT-4cited by 686
  8. 2019Neural Graph Collaborative Filteringcited by 661
  9. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
  10. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 360
  11. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 337
  12. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
38 more
  1. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  2. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  3. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  4. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  5. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  6. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 131
  7. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  8. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 116
  9. 2018Conversational Recommender Systemcited by 115
  10. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 114
  11. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  12. 2024Data-efficient Fine-tuning for LLM-based Recommendationcited by 104
  13. 2023CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendationcited by 100
  14. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
  15. 2023On Generative Agents in Recommendationcited by 90
  16. 2022Personalized Prompt Learning for Explainable Recommendationcited by 79
  17. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  18. 2023Towards Open-World Recommendation with Knowledge Augmentation from Large Language Modelscited by 74
  19. 2023Collaborative Large Language Model for Recommender Systemscited by 64
  20. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  21. 2023GenRec: Large Language Model for Generative Recommendationcited by 63
  22. 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Modelscited by 54
  23. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 53
  24. 2023Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestioncited by 43
  25. 2023E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendationcited by 39
  26. 2024Plug-in Diffusion Model for Sequential Recommendationcited by 37
  27. 2023Large Language Model Augmented Narrative Driven Recommendationscited by 31
  28. 2023A Large Language Model Enhanced Conversational Recommender Systemcited by 31
  29. 2024User Embedding Model for Personalized Language Promptingcited by 28
  30. 2023LLM4Vis: Explainable Visualization Recommendation using ChatGPTcited by 25
  31. 2023MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinementcited by 24
  32. 2023Generative Sequential Recommendation with GPTReccited by 21
  33. 2024INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuningcited by 20
  34. 2023Large Language Model Can Interpret Latent Space of Sequential Recommendercited by 19
  35. 2024Enhancing Recommendation Diversity by Re-ranking with Large Language Modelscited by 19
  36. 2023A Two-Stage Adaptation of Large Language Models for Text Rankingcited by 18
  37. 2023One Model for All: Large Language Models are Domain-Agnostic Recommendation Systemscited by 17
  38. 2024Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysiscited by 17

50 most cited of 64 All 64 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 4; 2026: 2.

12 papers.

  1. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  2. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  3. 2024Cognitive Biases in Large Language Models for News Recommendationcited by 8
  4. 2024AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomycited by 6
  5. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challengescited by 3
  6. 2024Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Spherecited by 1
  7. 2025The LLM Wears Prada: Analysing Gender Bias and Stereotypes through Online Shopping Datacited by 1
  8. 2024EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME contextcited by 0
  9. 2025Using item recommendations and LLMs in marketing email titlescited by 0
  10. 2025From latent factors to language: a user study on LLM-generated explanations for an inherently interpretable matrix-based recommender systemcited by 0
  11. 2026Give Users the Wheel: Towards Promptable Recommendation Paradigmcited by 0
  12. 2026Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond
  2. 2023How Can Recommender Systems Benefit from Large Language Models: A Survey
  3. 2024All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
  4. 2025Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
  5. 2024Aligning Large Language Models with Recommendation Knowledge
  6. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
  7. 2023Empowering Few-Shot Recommender Systems with Large Language Models -- Enhanced Representations
  8. 2024Large Language Model Driven Recommendation