Behavior Tokens Speak Louder: Disentangled Explainable Recommendation with Behavior Vocabulary
Existing interpretable recommendation methods rely on ID-based representations, which suffer from semantic ambiguity and poor compatibility with large language models (LLMs); moreover, user behaviors exhibit entangled multi-intent patterns, and collaborative signals are semantically misaligned with natural language. To address these issues, we propose a **behavior tokenization paradigm**: leveraging graph neural networks to learn structured user–item interaction representations, and employing vector-quantized variational autoencoders (VQ-VAEs) to disentangle macro-level interests from micro-level intentions, thereby constructing a transferable, graph-enhanced behavior lexicon. We further design a multi-level semantic supervision scheme and an LLM input embedding alignment mechanism—freezing LLM embeddings—to bridge behavioral signals with natural language semantics. Evaluated on three public benchmarks, our method significantly improves zero-shot recommendation performance, generates coherent and informative explanations, and yields behavior tokens with fine-grained interpretability and cross-domain transferability.