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

arXiv: https://arxiv.org/abs/2408.06276 · HTML: https://mlsanity.com/paper/2408.06276/

## Key facts

- Cited by 22 papers in the map (data through 14 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.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 9 of 9)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024)
- [Is ChatGPT a Good Recommender? A Preliminary Study](https://mlsanity.com/paper/2304.10149/) (2023)
- [CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation](https://mlsanity.com/paper/2310.19488/) (2023)
- [LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking](https://mlsanity.com/paper/2311.02089/) (2023)
- [LLMRec: Benchmarking Large Language Models on Recommendation Task](https://mlsanity.com/paper/2308.12241/) (2023)

## Built on by (most cited first, 22 of 22)

- [Reinforced Latent Reasoning for LLM-based Recommendation](https://mlsanity.com/paper/2505.19092/) (2025)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [R2ec: Towards Large Recommender Models with Reasoning](https://mlsanity.com/paper/2505.16994/) (2025)
- [LLM-based User Profile Management for Recommender System](https://mlsanity.com/paper/2502.14541/) (2025)
- [Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation](https://mlsanity.com/paper/2602.16587/) (2026)
- [Preference Discerning with LLM-Enhanced Generative Retrieval](https://mlsanity.com/paper/2412.08604/) (2024)
- [CTRL-Rec: Controlling Recommender Systems With Natural Language](https://mlsanity.com/paper/2510.12742/) (2025)
- [RPM: Reasoning-Level Personalization for Black-Box Large Language Models](https://mlsanity.com/paper/2505.21082/) (2025)
- [Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs](https://mlsanity.com/paper/2509.00698/) (2025)
- [IPQA: A Benchmark for Core Intent Identification in Personalized Question Answering](https://mlsanity.com/paper/2510.23536/) (2025)
- [HADSF: Aspect Aware Semantic Control for Explainable Recommendation](https://mlsanity.com/paper/2510.26994/) (2025)
- [Verifiable Reasoning for LLM-based Generative Recommendation](https://mlsanity.com/paper/2603.07725/) (2026)
- [RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation](https://mlsanity.com/paper/2610.14474/) (2026)
- [Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing](https://mlsanity.com/paper/2509.08329/) (2025)
- [SPECTRA: Revealing the Full Spectrum of User Preferences via Distributional LLM Inference](https://mlsanity.com/paper/2509.24189/) (2025)
- [Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders](https://mlsanity.com/paper/2604.07825/) (2026)
- [Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking](https://mlsanity.com/paper/2605.27429/) (2026)
- [ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modeling](https://mlsanity.com/paper/2606.24605/) (2026)
- [Bridging the Information Gap: Semantic Densification and Hindsight Distillation for Cold-Start Prediction](https://mlsanity.com/paper/2607.17070/) (2026)
- [The Recall Ceiling of LLM Recommendation Reranking](https://mlsanity.com/paper/2609.27953/) (2026)
- [Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation](https://mlsanity.com/paper/2610.11501/) (2026)
- [HEM-Med: A Hierarchical Agentic Memory Framework for Safe Multi-Center Medication Recommendation](https://mlsanity.com/paper/2610.20191/) (2026)

## 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.