Mingzhe Liu

Behavior Tokens Speak Louder: Disentangled Explainable Recommendation with Behavior Vocabulary

Recent advances in explainable recommendation 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 const