Behavior Tokens Speak Louder: Disentangled Explainable Recommendation with Behavior Vocabulary

TL;DR

BEAT introduces a behavior vocabulary that tokenizes user and item behaviors into interpretable sequences, enhancing explainable recommendation systems by improving zero-shot performance and explanation coherence.

Contribution

The paper proposes a novel behavior tokenization framework using vector-quantized autoencoding and semantic supervision, enabling better interpretability and transferability in explainable recommendation models.

Findings

BEAT improves zero-shot recommendation accuracy.

Behavior tokens capture fine-grained semantics.

The framework generates coherent, informative explanations.

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.