用社会学框架构建用户行为的三层模型，让大模型更懂个人偏好。
Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization
- 基于布迪厄理论，将用户行为分层为实践、惯习和场域
- 在LaMP基准上多任务表现提升，且结构可解释
- 轻量无感部署，适合需要个性化的大模型应用
大型语言模型在多个领域表现出色，但个性化输出仍面临挑战。现有方法多采用扁平化行为聚合，未显式建模行为背后的深层结构。本文借鉴皮埃尔·布迪厄的实践理论，提出PHF（Practice-Habitus-Field）框架，从三个层次重构个性化：个体行为作为实践，时间积累形成稳定倾向即惯习，相似用户间的共性规律构成场域。我们实现了一个轻量、模型无关的PHF_Compass系统，基于冻结的LLM构建。在语言模型个性化（LaMP）基准上的实验显示，该方法在多种任务中均取得持续改进，进一步分析验证了所学行为结构的可解释性与可扩展性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, yet personalizing their outputs to individual users remains an open challenge. Existing approaches predominantly adopt a flat behavioral paradigm, aggregating user behaviors without an explicit account of how they are organized into deeper behavioral structures. In this work, we draw on Pierre Bourdieu's Theory of Practice to propose PHF (Practice-Habitus-Field), a sociologically grounded framework that reconceptualizes LLM personalization through three hierarchical levels: individual behaviors as practices, their temporal accumulation into stable dispositions as habitus, and shared regularities across similar users as fields. We instantiate PHF through $\mathrm{PHF}_{\text{Compass}}$, a lightweight and model-agnostic implementation based on a frozen LLM. Experiments on the Language Model Personalization (LaMP) benchmark demonstrate consistent improvements across diverse tasks, while further analyses validate the interpretability and extensibility of the learned behavioral structures.
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