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：生成式检索通过将用户查询直接映射到产品语义标识符（SID），为电商搜索提供了新范式。然而，电商查询通常简短、嘈杂、属性密集，且与多个类别一致的产品相关联，导致自然语言购物意图与人工构建的物品SID之间存在显著的表示鸿沟。显式思维链（CoT）推理有助于弥合这一鸿沟，但其额外的生成成本难以与电商在线系统的低延迟需求相协调。为解决这一挑战，我们提出CaLIR（类别引导的潜在意图推理），一种针对电商生成式检索的类别引导潜在意图推理框架。CaLIR不在SID解码前生成显式文本推理过程，而是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的意图推理的自然支撑。具体而言，我们引入层次语义推理，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样本查询下的多样化意图路径。CaLIR进一步将查询特定的动态前缀字典树（由预索引的类别级字典树组合而成）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在检索效果与推理效率之间取得了比现有方法更好的平衡，同时在诱导层次结构和不同生成骨干网络上展现出可迁移性和鲁棒性。