Behavior Tokens Speak Louder: Disentangled Explainable Recommendation with Behavior Vocabulary

• URL: http://arxiv.org/abs/2512.15614v1
• Date: Wed, 17 Dec 2025 17:24:24 GMT
• Title: Behavior Tokens Speak Louder: Disentangled Explainable Recommendation with Behavior Vocabulary
• Authors: Xinshun Feng, Mingzhe Liu, Yi Qiao, Tongyu Zhu, Leilei Sun, Shuai Wang,
• Abstract summary: BEAT is a framework that tokenizes user and item behaviors into discrete, interpretable sequences.<n>We show that BEAT improves zero-shot recommendation performance while generating coherent and informative explanations.
• Score: 22.925582428795437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in explainable recommendations have explored the integration of language models to analyze natural language rationales for user-item interactions. Despite their potential, existing methods often rely on ID-based representations that obscure semantic meaning and impose structural constraints on language models, thereby limiting their applicability in open-ended scenarios. These challenges are intensified by the complex nature of real-world interactions, where diverse user intents are entangled and collaborative signals rarely align with linguistic semantics. To overcome these limitations, we propose BEAT, a unified and transferable framework that tokenizes user and item behaviors into discrete, interpretable sequences. We construct a behavior vocabulary via a vector-quantized autoencoding process that disentangles macro-level interests and micro-level intentions from graph-based representations. We then introduce multi-level semantic supervision to bridge the gap between behavioral signals and language space. A semantic alignment regularization mechanism is designed to embed behavior tokens directly into the input space of frozen language models. Experiments on three public datasets show that BEAT improves zero-shot recommendation performance while generating coherent and informative explanations. Further analysis demonstrates that our behavior tokens capture fine-grained semantics and offer a plug-and-play interface for integrating complex behavior patterns into large language models.

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