Behavior Tokens Speak Louder: Disentangled Explainable Recommendation with Behavior Vocabulary

2025-12-17

AI Summary

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.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Disentangles macro and micro user intents from graph representations
• Bridges behavioral signals with language space for explainable recommendations
• Enables zero-shot recommendation via interpretable behavior token sequences

Innovation

• Tokenizes behaviors into discrete interpretable sequences
• Constructs behavior vocabulary via vector-quantized autoencoding
• Embeds behavior tokens into frozen language models