mlsanityData to 14 Oct 2026

Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs

arXiv 2509.00698 ↗First posted 31 Aug 2025cs.CLcited by 2

On the map: Retrieval-augmented generation → RAG for recommendation

Abstract (arXiv)

Large language models (LLMs) have shown strong potential in recommendation tasks due to their strengths in language understanding, reasoning and knowledge integration. These capabilities are especially beneficial for review-based recommendation, which relies on semantically rich user-generated texts to reveal fine-grained user preferences and item attributes. However, effectively incorporating reviews into LLM-based recommendation remains challenging due to (1) inefficient to dynamically utilize user reviews under LLMs' constrained context windows, and (2) lacking effective mechanisms to prioritize reviews most relevant to the user's current decision context. To address these challenges, we propose RevBrowse, a review-driven recommendation framework inspired by the "browse-then-decide" decision process commonly observed in online user behavior. RevBrowse integrates user reviews into the LLM-based reranking process to enhance its ability to distinguish between candidate items. To improve the relevance and efficiency of review usage, we introduce PrefRAG, a retrieval-augmented module that disentangles user and item representations into structured forms and adaptively retrieves preference-relevant content conditioned on the target item. Extensive experiments on four Amazon review datasets demonstrate that RevBrowse achieves consistent and significant improvements over strong baselines, highlighting its generalizability and effectiveness in modeling dynamic user preferences. Furthermore, since the retrieval-augmented process is transparent, RevBrowse offers a certain level of interpretability by making visible which reviews influence the final recommendation.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RAG for recommendation, one of 15 directions in Retrieval-augmented generation. Builds on 21 of its 44 references that are in the map. First posted on arXiv 31 Aug 2025.

Builds on

21 of 44 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation18 Feb
  2. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation4 Jan
  3. 2024CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation11 Mar

Other references · 18

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  4. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  5. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  6. 2018Self-Attentive Sequential Recommendationcited by 1,184
  7. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  8. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  9. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  10. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  11. 2024Large Language Models: A Surveycited by 525
  12. 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
6 more
  1. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
  2. 2017Joint Deep Modeling of Users and Items Using Reviews for Recommendationcited by 84
  3. 2024Harnessing Large Language Models for Text-Rich Sequential Recommendationcited by 38
  4. 2024Review-driven Personalized Preference Reasoning with Large Language Models for Recommendationcited by 22
  5. 2025Foundations of Large Language Modelscited by 14
  6. 2018Attentive Aspect Modeling for Review-aware Recommendationcited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 4
  2. 2026TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
  2. 2025Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration
  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. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
  6. 2025Bridging Collaborative Filtering and Large Language Models with Dynamic Alignment, Multimodal Fusion and Evidence-grounded Explanations
  7. 2025ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation
  8. 2023PALR: Personalization Aware LLMs for Recommendation