  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.
