Beyond Matching: Category-Guided Latent Intent Reasoning for Generative Retrieval in E-Commerce

• URL: http://arxiv.org/abs/2606.07075v2
• Date: Wed, 10 Jun 2026 07:02:50 GMT
• Title: Beyond Matching: Category-Guided Latent Intent Reasoning for Generative Retrieval in E-Commerce
• Abstract summary: CaLIR is a category-guided latent intent reasoning framework for e-commerce generative retrieval.<n>We introduce hierarchical semantic reasoning to align latent states with category-level shopping intent.<n>Experiments on multilingual e-commerce search datasets show that CaLIR achieves a better balance between retrieval effectiveness and inference efficiency than existing methods.
• Score: 33.55727546262202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative retrieval offers a new paradigm for e-commerce search by mapping user queries directly to product Semantic Identifiers (SIDs). However, e-commerce queries are often short, noisy, attribute-heavy, and associated with multiple category-consistent products, creating a substantial representation gap between natural-language shopping intent and artificially constructed item SIDs. Explicit Chain-of-Thought (CoT) reasoning can help bridge this gap, but its extra generation cost is difficult to reconcile with the low-latency requirements of online e-commerce systems. To address this challenge, we propose CaLIR (Category-guided Latent Intent Reasoning), a category-guided latent intent reasoning framework for e-commerce generative retrieval. Rather than generating explicit textual rationales, CaLIR learns continuous latent intent states before SID decoding and uses product category hierarchies as a natural scaffold for coarse-to-fine intent reasoning. Specifically, we introduce hierarchical semantic reasoning to align latent states with category-level shopping intent, and query-wise reasoning enhancement to model diverse intent paths under multi-positive queries. CaLIR further combines a query-specific dynamic prefix trie, assembled from pre-indexed category-level tries, with reasoning-aware constrained decoding. Experiments on multilingual e-commerce search datasets show that CaLIR achieves a better balance between retrieval effectiveness and inference efficiency than existing methods, while also demonstrating transferability and robustness across induced hierarchies and different generative backbones.

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