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.

翻译：生成式检索为电子商务搜索提供了新的范式，通过将用户查询直接映射到产品语义标识符（SIDs）。然而，电商查询通常简短、嘈杂、属性丰富，且与多个类别一致的产品相关联，这在自然语言购物意图与人工构建的商品SIDs之间造成了显著的表示差距。显式的思维链（CoT）推理有助于弥合这一差距，但其额外的生成成本难以与在线电商系统的低延迟要求相协调。为应对这一挑战，我们提出了CaLIR（类别引导的潜在意图推理），这是一个用于电商生成式检索的类别引导潜在意图推理框架。与生成显式文本理由不同，CaLIR在SID解码之前学习连续的潜在意图状态，并利用产品类别层次结构作为从粗到细意图推理的自然支架。具体而言，我们引入分层语义推理将潜在状态与类别级购物意图对齐，并通过查询级推理增强来建模多正例查询下的多样意图路径。CaLIR进一步将查询特定的动态前缀Trie（由预索引的类别级Trie组装而成）与推理感知的约束解码相结合。在多语言电商搜索数据集上的实验表明，CaLIR在检索有效性和推理效率之间实现了比现有方法更好的平衡，同时在诱导层次和不同生成骨干网络上展现出可迁移性和鲁棒性。