PerFairX：大型语言模型推荐中公平性与个性之间是否存在平衡？
PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?
AI总结：
本研究提出PerFairX框架，量化LLM推荐中个性与公平性的权衡，发现个性提示提升心理对齐但加剧公平差异，为平衡两者提供基准。
摘要：
将大型语言模型（LLMs）集成到推荐系统中，通过基于提示的交互实现了零样本、基于个性的个性化推荐，为用户中心的推荐提供了一种新范式。然而，通过OCEAN模型引入用户个性特征，凸显了实现心理对齐与确保人口统计公平性之间的关键张力。为解决这一问题，我们提出了PerFairX，一个统一的评估框架，旨在量化LLM生成推荐中个性化与人口统计公平性之间的权衡。通过在不同用户画像上使用中性和个性敏感提示，我们在电影（MovieLens 10M）和音乐（Last.fm 360K）数据集上对两个最先进的LLM——ChatGPT和DeepSeek——进行了基准测试。我们的结果表明，个性感知提示显著改善了对个体特质的对齐，但可能加剧跨人口统计群体的公平性差异。具体而言，DeepSeek实现了更强的心理契合度，但对提示变化表现出更高的敏感性，而ChatGPT则提供稳定但个性化程度较低的输出。PerFairX提供了一个有原则的基准，以指导开发既公平又具有心理感知的基于LLM的推荐系统，有助于在持续学习环境中创建包容性、用户中心的人工智能应用。
英文摘要：
The integration of Large Language Models (LLMs) into recommender systems has enabled zero-shot, personality-based personalization through prompt-based interactions, offering a new paradigm for user-centric recommendations. However, incorporating user personality traits via the OCEAN model highlights a critical tension between achieving psychological alignment and ensuring demographic fairness. To address this, we propose PerFairX, a unified evaluation framework designed to quantify the trade-offs between personalization and demographic equity in LLM-generated recommendations. Using neutral and personality-sensitive prompts across diverse user profiles, we benchmark two state-of-the-art LLMs, ChatGPT and DeepSeek, on movie (MovieLens 10M) and music (Last.fm 360K) datasets. Our results reveal that personality-aware prompting significantly improves alignment with individual traits but can exacerbate fairness disparities across demographic groups. Specifically, DeepSeek achieves stronger psychological fit but exhibits higher sensitivity to prompt variations, while ChatGPT delivers stable yet less personalized outputs. PerFairX provides a principled benchmark to guide the development of LLM-based recommender systems that are both equitable and psychologically informed, contributing to the creation of inclusive, user-centric AI applications in continual learning contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
