Evaluating Fairness in Recommender Systems Powered by Large Language Models: A Comprehensive Framework

This paper presents a comprehensive framework, FairEvalLLM, for evaluating fairness in recommender systems powered by large language models (RecLLMs). The framework incorporates various fairness notions, including sensitivity to user attributes, intrinsic fairness, and discussions of fairness based on underlying benefits.