PrefPalette：基于潜在属性的个性化偏好建模

PrefPalette: Personalized Preference Modeling with Latent Attributes

July 17, 2025

作者: Shuyue Stella Li, Melanie Sclar, Hunter Lang, Ansong Ni, Jacqueline He, Puxin Xu, Andrew Cohen, Chan Young Park, Yulia Tsvetkov, Asli Celikyilmaz

cs.AI

摘要

个性化AI系统不仅需要理解用户的偏好，还需洞察这些偏好背后的原因——然而，当前的偏好模型通常将人类判断视为一个黑箱。我们引入了PrefPalette框架，该框架将偏好分解为多个属性维度，并以人类可解释的方式针对不同社交社区的价值进行偏好预测。PrefPalette通过两种方式实现了认知科学中的多属性决策原则：(1) 可扩展的反事实属性合成步骤，通过生成合成训练数据来隔离单个属性的影响（如正式性、幽默感、文化价值观）；(2) 基于注意力的偏好建模，学习不同社交社区如何动态权衡这些属性。这一方法超越了聚合偏好建模，捕捉到了驱动人类判断的多样化评估框架。在在线平台Reddit的45个社交社区上评估时，PrefPalette的平均预测准确率比GPT-4o高出46.6%。除了预测性能的提升，PrefPalette还揭示了直观的、社区特定的特征：学术社区重视详尽性和启发性，冲突导向的社区看重讽刺和直接性，而支持型社区则强调同理心。通过建模人类判断的属性中介结构，PrefPalette不仅提供了更优的偏好建模，还带来了透明、可解释的洞察，为开发更值得信赖、价值感知的个性化应用迈出了第一步。

English

Personalizing AI systems requires understanding not just what users prefer, but the reasons that underlie those preferences - yet current preference models typically treat human judgment as a black box. We introduce PrefPalette, a framework that decomposes preferences into attribute dimensions and tailors its preference prediction to distinct social community values in a human-interpretable manner. PrefPalette operationalizes a cognitive science principle known as multi-attribute decision making in two ways: (1) a scalable counterfactual attribute synthesis step that involves generating synthetic training data to isolate for individual attribute effects (e.g., formality, humor, cultural values), and (2) attention-based preference modeling that learns how different social communities dynamically weight these attributes. This approach moves beyond aggregate preference modeling to capture the diverse evaluation frameworks that drive human judgment. When evaluated on 45 social communities from the online platform Reddit, PrefPalette outperforms GPT-4o by 46.6% in average prediction accuracy. Beyond raw predictive improvements, PrefPalette also shed light on intuitive, community-specific profiles: scholarly communities prioritize verbosity and stimulation, conflict-oriented communities value sarcasm and directness, and support-based communities emphasize empathy. By modeling the attribute-mediated structure of human judgment, PrefPalette delivers both superior preference modeling and transparent, interpretable insights, and serves as a first step toward more trustworthy, value-aware personalized applications.