@inproceedings{wang-etal-2026-beyond-isolated,
title = "Beyond Isolated Behaviors: Hierarchical User Modeling for {LLM} Personalization",
author = "Wang, Liang and
Mou, Xinyi and
Liu, Xiaoyou and
Wang, Tiannan and
Wang, Yuqing and
Wei, Zhongyu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.259/",
pages = "5432--5448",
ISBN = "979-8-89176-462-0",
abstract = "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 PHF 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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%0 Conference Proceedings
%T Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization
%A Wang, Liang
%A Mou, Xinyi
%A Liu, Xiaoyou
%A Wang, Tiannan
%A Wang, Yuqing
%A Wei, Zhongyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-beyond-isolated
%X 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 PHF 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.
%U https://aclanthology.org/2026.findings-emnlp.259/
%P 5432-5448
Markdown (Informal)
[Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization](https://aclanthology.org/2026.findings-emnlp.259/) (Wang et al., Findings 2026)
ACL