mlsanityData to 7 Oct 2026

Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation

arXiv 2408.06276 ↗First posted 12 Aug 2024cs.CLcited by 20

On the map: LLM reasoning → LLM reasoning for recommendation

Abstract (arXiv)

Recent advancements in Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks, generating significant interest in their application to recommendation systems. However, existing methods have not fully capitalized on the potential of LLMs, often constrained by limited input information or failing to fully utilize their advanced reasoning capabilities. To address these limitations, we introduce EXP3RT, a novel LLM-based recommender designed to leverage rich preference information contained in user and item reviews. EXP3RT is basically fine-tuned through distillation from a teacher LLM to perform three key tasks in order: EXP3RT first extracts and encapsulates essential subjective preferences from raw reviews, aggregates and summarizes them according to specific criteria to create user and item profiles. It then generates detailed step-by-step reasoning followed by predicted rating, i.e., reasoning-enhanced rating prediction, by considering both subjective and objective information from user/item profiles and item descriptions. This personalized preference reasoning from EXP3RT enhances rating prediction accuracy and also provides faithful and reasonable explanations for recommendation. Extensive experiments show that EXP3RT outperforms existing methods on both rating prediction and candidate item reranking for top-k recommendation, while significantly enhancing the explainability of recommendation systems.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning. Builds on 9 of its 36 references that are in the map. First posted on arXiv 12 Aug 2024.

Builds on

9 of 36 references.

References · 9

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2023A Survey on Large Language Models for Recommendationcited by 378
  4. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 241
  5. 2024Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoderscited by 213
  6. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 192
  7. 2023CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendationcited by 99
  8. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 52
  9. 2023LLMRec: Benchmarking Large Language Models on Recommendation Taskcited by 29

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 10; 2026: 8.

20 papers.

Sort:

By area:

  1. 2025Reinforced Latent Reasoning for LLM-based Recommendationcited by 46
  2. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  3. 2025R2ec: Towards Large Recommender Models with Reasoningcited by 13
  4. 2025LLM-based User Profile Management for Recommender Systemcited by 9
  5. 2026Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendationcited by 9
  6. 2024Preference Discerning with LLM-Enhanced Generative Retrievalcited by 6
  7. 2025CTRL-Rec: Controlling Recommender Systems With Natural Languagecited by 4
  8. 2025RPM: Reasoning-Level Personalization for Black-Box Large Language Modelscited by 2
  9. 2025Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMscited by 2
  10. 2025IPQA: A Benchmark for Core Intent Identification in Personalized Question Answeringcited by 2
  11. 2025HADSF: Aspect Aware Semantic Control for Explainable Recommendationcited by 2
  12. 2026Verifiable Reasoning for LLM-based Generative Recommendationcited by 2
8 more
  1. 2025Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusingcited by 0
  2. 2025SPECTRA: Revealing the Full Spectrum of User Preferences via Distributional LLM Inferencecited by 0
  3. 2026Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenderscited by 0
  4. 2026Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Rerankingcited by 0
  5. 2026ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modelingcited by 0
  6. 2026Bridging the Information Gap: Semantic Densification and Hindsight Distillation for Cold-Start Predictioncited by 0
  7. 2026The Recall Ceiling of LLM Recommendation Rerankingcited by 0
  8. 2026Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026P3Rec: Distilling Prior--Posterior Preference Reasoning for LLM-based Recommendation
  2. 2024Leveraging LLM Reasoning Enhances Personalized Recommender Systems
  3. 2024ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning
  4. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  5. 2026Reasoning to Rank: An End-to-End Solution for Exploiting Large Language Models for Recommendation
  6. 2025What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context
  7. 2025MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs
  8. 2025Can Large Language Models Understand Preferences in Personalized Recommendation?