# CAT-ID2: Category-Tree Integrated Document Identifier Learning for Generative Retrieval In E-commerce

arXiv: https://arxiv.org/abs/2511.01461 · HTML: https://mlsanity.com/paper/2511.01461/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2025.

## Built on by (most cited first, 5 of 5)

- [Multi-Aspect Cross-modal Quantization for Generative Recommendation](https://mlsanity.com/paper/2511.15122/) (2025)
- [Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL](https://mlsanity.com/paper/2605.14434/) (2026)
- [PRISM: Purified Representation and Integrated Semantic Modeling for Generative Sequential Recommendation](https://mlsanity.com/paper/2601.16556/) (2026)
- [Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval](https://mlsanity.com/paper/2608.30553/) (2026)
- [Exploring Bottom-Up Clustering for Creating Semantic IDs](https://mlsanity.com/paper/2609.08310/) (2026)

## Abstract (arXiv)

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-ID2), incorporating prior category information into the semantic IDs. CAT-ID2 includes three key modules: a Hierarchical Class Constraint Loss to integrate category information layer by layer during quantization, a Cluster Scale Constraint Loss for uniform ID token distribution, and a Dispersion Loss to improve the distinction of reconstructed documents. These components enable CAT-ID2 to generate IDs that make similar documents more alike while preserving the uniqueness of different documents' representations. Extensive offline and online experiments confirm the effectiveness of our method, with online A/B tests showing a 0.33% increase in average orders per thousand users for ambiguous intent queries and 0.24% for long-tail queries.