探索大模型推荐中个性与公平的平衡，发现个性推荐可能加剧不公平。
PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?
- 构建统一评估框架，量化个性推荐与公平性的权衡
- 性格提示提升心理契合度，但放大不同群体间的推荐差距
- 适合关注AI公平性与个性化平衡的研究者和开发者
将大语言模型（LLMs）融入推荐系统，可通过基于提示的交互实现零样本、个性化的用户定制，开启以用户为中心的新范式。然而，通过OCEAN模型引入用户人格特质，暴露出心理契合度与人口统计公平性之间的关键矛盾。为此，我们提出PerFairX，一个统一的评估框架，用于量化LLM生成推荐中个性化与人口公平性的权衡。在多样用户画像下，使用中性与人格敏感提示，我们在电影（MovieLens 10M）和音乐（Last.fm 360K）数据集上对两种先进LLM（ChatGPT和DeepSeek）进行基准测试。结果表明，人格感知提示显著提升个体特征契合度，但可能加剧不同人口群体间的公平性差异。具体而言，DeepSeek在心理契合度上表现更强，但对提示变化更敏感；而ChatGPT输出稳定但个性化程度较低。PerFairX为开发既公平又具心理洞察力的LLM推荐系统提供原则性基准，助力持续学习场景下包容性、用户中心的AI应用建设。
原文摘要 · Abstract (English)
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.
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