Externalizing Social-Cognitive Structures for User Modeling: Toward Theory-Driven Profiling with LLMs

Abstract

In this paper, we propose TRIPLE (TPB-dRIven Profiling with LLM rEfinement), a dynamic profiling framework that incorporates the Theory of Planned Behavior (TPB) into user profile modeling. Our method (1) extracts TPB components from historical text data to construct an initial user profile, (2) iteratively refines this profile by analyzing discrepancies between predicted and actual behaviors, and (3) continuously updates the user's state by incorporating newly arriving text. We evaluate TRIPLE on the LaMP datasets, focusing on rating prediction and personalized tweet paraphrasing tasks, using multiple open-source large language models. Experimental results demonstrate that TRIPLE consistently outperforms existing profiling methods across all evaluation settings. Qualitative analysis confirms that TRIPLE captures the psychological and social mechanisms underlying users' product evaluation and description. These findings provide empirical evidence that theory- driven user profiling can significantly improve personalization performance in recommender systems and related applications. Our implementation and examples of generated profiles are available at https://yestaehyung.github.io/cikm25-triple/.