mlsanityData to 7 Oct 2026

RDRec: Rationale Distillation for LLM-based Recommendation

arXiv 2405.10587 ↗First posted 17 May 2024cs.CL cs.IRcited by 21

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Abstract (arXiv)

Large language model (LLM)-based recommender models that bridge users and items through textual prompts for effective semantic reasoning have gained considerable attention. However, few methods consider the underlying rationales behind interactions, such as user preferences and item attributes, limiting the reasoning capability of LLMs for recommendations. This paper proposes a rationale distillation recommender (RDRec), a compact model designed to learn rationales generated by a larger language model (LM). By leveraging rationales from reviews related to users and items, RDRec remarkably specifies their profiles for recommendations. Experiments show that RDRec achieves state-of-the-art (SOTA) performance in both top-N and sequential recommendations. Our source code is released at https://github.com/WangXFng/RDRec.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 36 of its 48 references that are in the map. First posted on arXiv 17 May 2024.

Builds on

36 of 48 references.

References · 36

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2018Self-Attentive Sequential Recommendationcited by 1,169
  5. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  6. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  7. 2016Wide & Deep Learning for Recommender Systemscited by 745
  8. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 710
  9. 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
  10. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 473
  11. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  12. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 351
24 more
  1. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  2. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 286
  3. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matterscited by 221
  4. 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
  5. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendationcited by 171
  6. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  7. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  8. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 137
  9. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 130
  10. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 126
  11. 2019Hierarchical Gating Networks for Sequential Recommendationcited by 101
  12. 2023Reasoning Implicit Sentiment with Chain-of-Thought Promptingcited by 79
  13. 2023Leveraging Large Language Models for Sequential Recommendationcited by 78
  14. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  15. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 52
  16. 2023Leveraging Large Language Models for Pre-trained Recommender Systemscited by 42
  17. 2022Sequential Recommendation via Stochastic Self-Attentioncited by 37
  18. 2023Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendationcited by 29
  19. 2023Explainable Recommendation with Personalized Review Retrieval and Aspect Learningcited by 19
  20. 2023Large Language Model Can Interpret Latent Space of Sequential Recommendercited by 19
  21. 2023Language-Guided Music Recommendation for Video via Prompt Analogiescited by 18
  22. 2023DataFinder: Scientific Dataset Recommendation from Natural Language Descriptionscited by 15
  23. 2023EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verificationcited by 9
  24. 2024NFARec: A Negative Feedback-Aware Recommender Modelcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 10; 2026: 6.

21 papers.

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  1. 2025A Survey of Multimodal Retrieval-Augmented Generationcited by 37
  2. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  3. 2024AD-LLM: Benchmarking Large Language Models for Anomaly Detectioncited by 25
  4. 2025Towards Agentic Recommender Systems in the Era of Multimodal Large Language Modelscited by 25
  5. 2024MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretationcited by 18
  6. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  7. 2024Enhancing High-order Interaction Awareness in LLM-based Recommender Modelcited by 8
  8. 2025CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Languagecited by 8
  9. 2024KVPruner: Structural Pruning for Faster and Memory-Efficient Large Language Modelscited by 6
  10. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challengescited by 3
  11. 2025Tree of Preferences for Diversified Recommendationcited by 2
  12. 2025Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?cited by 1
9 more
  1. 2025Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenderscited by 1
  2. 2025Routing Distilled Knowledge via Mixture of LoRA Experts for Large Language Model based Bundle Generationcited by 1
  3. 2026Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedbackcited by 1
  4. 2025From What to Why: Thought-Space Recommendation with Small Language Modelscited by 0
  5. 2026Sensory-Aware Sequential Recommendation via Review-Distilled Representationscited by 0
  6. 2026Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Mergingcited by 0
  7. 2026GOD: Enhancing Generalization via Deep Grafting for Sequential Recommendationcited by 0
  8. 2026Decision Shifts, Lost Label Functionality, and an Inconclusive Grounding Audit in Correctness-Gated Multi-Teacher Distillationcited by 0
  9. 2026P3Rec: Distilling Prior--Posterior Preference Reasoning for LLM-based Recommendationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025ThinkRec: Thinking-based recommendation via LLM
  2. 2023How Can Recommender Systems Benefit from Large Language Models: A Survey
  3. 2025Towards Trustworthy LLM-Based Recommendation via Rationale Integration
  4. 2024ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning
  5. 2024Can Small Language Models be Good Reasoners for Sequential Recommendation?
  6. 2025DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation
  7. 2026Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation
  8. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review