XRec: Large Language Models for Explainable Recommendation

TL;DR

XRec leverages large language models to enhance explainability in recommender systems, providing comprehensive, meaningful explanations for user preferences and item recommendations, thereby improving transparency and user understanding.

Contribution

The paper introduces XRec, a model-agnostic framework that integrates LLMs with collaborative signals to generate explainable recommendations, advancing the interpretability of complex recommender models.

Findings

XRec outperforms baseline explainability methods in generating meaningful explanations.

The framework effectively captures complex user-item interaction patterns.

XRec demonstrates strong generalization across different recommendation scenarios.

Abstract

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.