Problem
Research questions and friction points this paper is trying to address.
fairness
recommender systems
large language models
bias
evaluation
Rethinking Fairness in LLM-Based Recommender Systems: A Survey
🤖 AI Summary
本文探讨了基于大语言模型的推荐系统中的公平性问题，通过分析偏见机制和公平目标，综述了评估和缓解策略。
📝 Abstract
Large Language Models (LLMs) are reshaping recommender systems by enabling more semantic, generative, and interactive recommendation pipelines. However, this shift also introduces new fairness challenges, as biases may arise from pretrained knowledge, prompts, generated explanations, decoding strategies, and feedback loops. This survey provides a systematic review of fairness in LLM-based recommender systems (LLM4Rec), organizing existing studies through a two-dimensional view of bias mechanisms and fairness targets, together with a structured overview of the evaluation landscape and mitigation strategies. We further connect fairness with broader trustworthy concerns, including explainability, privacy, robustness, and controllability. To the best of our knowledge, this is the first survey specifically focused on fairness in LLM4Rec, aiming to provide a structured foundation for future research on comprehensive and reliable fairness evaluation in LLM4Rec.
Innovation
Methods, ideas, or system contributions that make the work stand out.
fairness
large language models
recommender systems
bias mechanisms
trustworthy AI
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