# XRec: Large Language Models for Explainable Recommendation

arXiv: https://arxiv.org/abs/2406.02377 · HTML: https://mlsanity.com/paper/2406.02377/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [Representation Learning with Large Language Models for Recommendation](https://mlsanity.com/paper/2310.15950/) (2023)
- [Personalized Transformer for Explainable Recommendation](https://mlsanity.com/paper/2105.11601/) (2021)
- [STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1905.13129/) (2019)
- [Neural Rating Regression with Abstractive Tips Generation for Recommendation](https://mlsanity.com/paper/1708.00154/) (2017)
- [Neural Collaborative Reasoning](https://mlsanity.com/paper/2005.08129/) (2020)
- [Automated Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2303.07797/) (2023)
- [Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs](https://mlsanity.com/paper/2310.18152/) (2023)
- [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining](https://mlsanity.com/paper/2202.13469/) (2022)
- [Personalized Showcases: Generating Multi-Modal Explanations for Recommendations](https://mlsanity.com/paper/2207.00422/) (2022)
- [Knowledge Graph Self-Supervised Rationalization for Recommendation](https://mlsanity.com/paper/2307.02759/) (2023)
- [Generative Contrastive Graph Learning for Recommendation](https://mlsanity.com/paper/2307.05100/) (2023)

## Built on by (most cited first, 30 of 35)

- [G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation](https://mlsanity.com/paper/2502.12586/) (2025)
- [MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation](https://mlsanity.com/paper/2412.08847/) (2024)
- [Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models](https://mlsanity.com/paper/2505.17051/) (2025)
- [Towards Explainable Temporal User Profiling with LLMs](https://mlsanity.com/paper/2505.00886/) (2025)
- [From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context](https://mlsanity.com/paper/2508.07117/) (2025)
- [Reasoning over User Preferences: Knowledge Graph-Augmented LLMs for Explainable Conversational Recommendations](https://mlsanity.com/paper/2411.14459/) (2024)
- [Graph Foundation Models for Recommendation: A Comprehensive Survey](https://mlsanity.com/paper/2502.08346/) (2025)
- [GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation](https://mlsanity.com/paper/2410.11841/) (2024)
- [A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges](https://mlsanity.com/paper/2507.21117/) (2025)
- [Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking](https://mlsanity.com/paper/2501.00244/) (2024)
- [CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection](https://mlsanity.com/paper/2501.04510/) (2025)
- [Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines](https://mlsanity.com/paper/2503.08051/) (2025)
- [Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation](https://mlsanity.com/paper/2507.09188/) (2025)
- [Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems](https://mlsanity.com/paper/2411.13865/) (2024)
- [Effectiveness of LLMs in Temporal User Profiling for Recommendation](https://mlsanity.com/paper/2511.00176/) (2025)
- [Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing](https://mlsanity.com/paper/2602.21756/) (2026)
- [Node Role-Guided LLMs for Dynamic Graph Clustering](https://mlsanity.com/paper/2603.13799/) (2026)
- [RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation](https://mlsanity.com/paper/2610.14474/) (2026)
- [Community Search in Time-dependent Road-social Attributed Networks](https://mlsanity.com/paper/2505.12309/) (2025)
- [Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI](https://mlsanity.com/paper/2506.12240/) (2025)
- [CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models](https://mlsanity.com/paper/2506.17281/) (2025)
- [How Reliable are LLMs for Reasoning on the Re-ranking task?](https://mlsanity.com/paper/2508.18444/) (2025)
- [Few-Shot and Training-Free Review Generation via Conversational Prompting](https://mlsanity.com/paper/2509.20805/) (2025)
- [Reproducibility Study of "XRec: Large Language Models for Explainable Recommendation"](https://mlsanity.com/paper/2510.06275/) (2025)
- [The Oracle and The Prism: A Decoupled and Efficient Framework for Generative Recommendation Explanation](https://mlsanity.com/paper/2511.16543/) (2025)
- [Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research](https://mlsanity.com/paper/2602.17450/) (2026)
- [Sensory-Aware Sequential Recommendation via Review-Distilled Representations](https://mlsanity.com/paper/2603.02709/) (2026)
- [MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation](https://mlsanity.com/paper/2604.03666/) (2026)
- [Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation](https://mlsanity.com/paper/2606.20554/) (2026)
- [CALMRec: Causally Aligned Language Memory for Long-Horizon Recommendation](https://mlsanity.com/paper/2607.23647/) (2026)

## Abstract (arXiv)

Recommender systems help users navigate information overload by providing personalized recommendations aligned with their preferences. Collaborative Filtering (CF) is a widely adopted approach, but while advanced techniques like graph neural networks (GNNs) and self-supervised learning (SSL) have enhanced CF models for better user representations, they often lack the ability to provide explanations for the recommended items. Explainable recommendations aim to address this gap by offering transparency and insights into the recommendation decision-making process, enhancing users' understanding. This work leverages the language capabilities of Large Language Models (LLMs) to push the boundaries of explainable recommender systems. We introduce a model-agnostic framework called XRec, which enables LLMs to provide comprehensive explanations for user behaviors in recommender systems. By integrating collaborative signals and designing a lightweight collaborative adaptor, the framework empowers LLMs to understand complex patterns in user-item interactions and gain a deeper understanding of user preferences. Our extensive experiments demonstrate the effectiveness of XRec, showcasing its ability to generate comprehensive and meaningful explanations that outperform baseline approaches in explainable recommender systems. We open-source our model implementation at https://github.com/HKUDS/XRec.