mlsanityData to 14 Oct 2026

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems

arXiv 2507.17249 ↗First posted 23 Jul 2025cs.IRcited by 16

On the map: LLM reasoning → LLM reasoning for recommendation

Abstract (arXiv)

Harnessing Large Language Models (LLMs) for recommendation systems has emerged as a prominent avenue, drawing substantial research interest. However, existing approaches primarily involve basic prompt techniques for knowledge acquisition, which resemble System-1 thinking. This makes these methods highly sensitive to errors in the reasoning path, where even a small mistake can lead to an incorrect inference. To this end, in this paper, we propose R4ec, a reasoning, reflection and refinement framework that evolves the recommendation system into a weak System-2 model. Specifically, we introduce two models: an actor model that engages in reasoning, and a reflection model that judges these responses and provides valuable feedback. Then the actor model will refine its response based on the feedback, ultimately leading to improved responses. We employ an iterative reflection and refinement process, enabling LLMs to facilitate slow and deliberate System-2-like thinking. Ultimately, the final refined knowledge will be incorporated into a recommendation backbone for prediction. We conduct extensive experiments on Amazon-Book and MovieLens-1M datasets to demonstrate the superiority of R4ec. We also deploy R4ec on a large scale online advertising platform, showing 2.2% increase of revenue. Furthermore, we investigate the scaling properties of the actor model and reflection model.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning. Builds on 37 of its 59 references that are in the map. First posted on arXiv 23 Jul 2025.

Builds on

37 of 59 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model26 May
  2. 2024Rethinking Large Language Model Architectures for Sequential Recommendations14 Feb

Other references · 35

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  6. 2024Qwen2.5 Technical Reportcited by 4,942
  7. 2020Longformer: The Long-Document Transformercited by 3,413
  8. 2023Qwen Technical Reportcited by 3,344
  9. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  10. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  11. 2023Teaching Large Language Models to Self-Debugcited by 988
  12. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
23 more
  1. 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
  2. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 676
  3. 2024Training Language Models to Self-Correct via Reinforcement Learningcited by 426
  4. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoningcited by 391
  5. 2022Generating Sequences by Learning to Self-Correctcited by 277
  6. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  7. 2023REFINER: Reasoning Feedback on Intermediate Representationscited by 205
  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. 2023Generative Judge for Evaluating Alignmentcited by 169
  11. 2024Recursive Introspection: Teaching Language Model Agents How to Self-Improvecited by 165
  12. 2024Learnable Item Tokenization for Generative Recommendationcited by 110
  13. 2023Improving Language Models via Plug-and-Play Retrieval Feedbackcited by 87
  14. 2023CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generationcited by 66
  15. 2024Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervisioncited by 60
  16. 2023LLaRA: Large Language-Recommendation Assistantcited by 56
  17. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  18. 2024LeDex: Training LLMs to Better Self-Debug and Explain Codecited by 40
  19. 2025A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoningcited by 40
  20. 2024Critic-CoT: Boosting the reasoning abilities of large language model via Chain-of-thoughts Criticcited by 28
  21. 2024Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysiscited by 17
  22. 2025ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detectioncited by 17
  23. 2024Finetuning Large Language Model for Personalized Rankingcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 11.

16 papers.

Sort:

By area:

  1. 2025OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking Systemcited by 18
  2. 2026GR2: Generative Reasoning Re-rankercited by 11
  3. 2025TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendationcited by 7
  4. 2025Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendationcited by 6
  5. 2026RaMem: Contextual Reinstatement for Long-term Agentic Memorycited by 6
  6. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 4
  7. 2026Memory Retrieval for Changing Preferencescited by 3
  8. 2026Verifiable Reasoning for LLM-based Generative Recommendationcited by 2
  9. 2026Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendationcited by 2
  10. 2025ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Userscited by 1
  11. 2025Selection and Exploitation of High-Quality Knowledge from Large Language Models for Recommendationcited by 0
  12. 2026VLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Modelscited by 0
4 more
  1. 2026LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domainscited by 0
  2. 2026TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendationcited by 0
  3. 2026GR2 Technical Reportcited by 0
  4. 2026LaRec: Unleashing LLM-based Latent Reasoning for Generative Recommendationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025MGFRec: Towards Reinforced Reasoning Recommendation with Multiple Groundings and Feedback
  2. 2024GOT4Rec: Graph of Thoughts for Sequential Recommendation
  3. 2026Reinforced Preference Optimization for Reasoning-Augmented Recommendations
  4. 2025R2ec: Towards Large Recommender Models with Reasoning
  5. 2025Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation
  6. 2024Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
  7. 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation
  8. 2025SRLF: An Agent-Driven Set-Wise Reflective Learning Framework for Sequential Recommendation