CAT-ID\(^2\): Category-tree Integrated Document Identifier Learning For Generative Retrieval In E-commerce

Abstract

Generative retrieval (GR) has gained significant attention as an effective paradigm that integrates the capabilities of large language models (LLMs). It generally consists of two stages: constructing discrete semantic identifiers (IDs) for documents and retrieving documents by autoregressively generating ID tokens. The core challenge in GR is how to construct document IDs (DocIDS) with strong representational power. Good IDs should exhibit two key properties: similar documents should have more similar IDs, and each document should maintain a distinct and unique ID. However, most existing methods ignore native category information, which is common and critical in E-commerce. Therefore, we propose a novel ID learning method, CAtegory-Tree Integrated Document IDentifier (CAT-ID\(^2\)), incorporating prior category information into the semantic IDs. CAT-ID\(^2\) includes three key modules: a Hierarchical Class Constraint Loss to integrate category information layer by layer during quantiza

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