Recommendation Explanation

Recommendation explanation focuses on providing understandable justifications for why a recommender system suggests particular items, aiming to increase user trust and satisfaction. Current research heavily utilizes large language models (LLMs) and diffusion models to generate natural language explanations, often incorporating knowledge graphs to provide context and personalized prompts to enhance relevance. Evaluation methods are a key focus, with studies exploring both human-based assessments and automated approaches using LLMs to evaluate explanation quality, ultimately seeking to improve the transparency and effectiveness of recommender systems across various applications.