Faceted search acts as a critical bridge for navigating massive ecommerce catalogs, yet traditional systems rely on static rule-based extraction or statistical ranking, struggling with emerging vocabulary, semantic gaps, and a disconnect between facet selection and underlying retrieval. In this paper, we introduce GenFacet, an industrial-grade, end-to-end generative framework deployed at JD.com. GenFacet reframes faceted search as two coupled generative tasks within a unified Large Language Model: Context-Aware Facet Generation, which dynamically synthesizes trend-responsive navigation options, and Intent-Driven Query Rewriting, which translates user interactions into precise search queries to close the retrieval loop. To bridge the gap between generative capabilities and search utility, we propose a novel multi-task training pipeline combining teacher-student distillation with GRPO. This aligns the model with complex user preferences by directly optimizing for downstream search satisfaction. Validated on China's largest selfoperated e-commerce platform via rigorous offline evaluations and online A/B tests, GenFacet demonstrated substantial improvements. Specifically, online results reveal a relative increase of 42.0% in facet Click-Through Rate (CTR) and 2.0% in User Conversion Rate (UCVR). These outcomes provide strong evidence of the benefits of generative methods for improving query understanding and user engagement in large-scale information retrieval systems.

翻译：分面搜索是导航海量电子商务目录的关键桥梁，然而传统系统依赖基于规则的静态提取或统计排序，难以应对新兴词汇、语义鸿沟以及分面选择与底层检索之间的脱节问题。本文提出GenFacet——一个在京东部署的工业级端到端生成式框架。该框架将分面搜索重新定义为统一大语言模型内的两个耦合生成任务：上下文感知分面生成（动态合成响应趋势的导航选项）与意图驱动查询重写（将用户交互转化为精确搜索查询以闭合检索循环）。为弥合生成能力与搜索效用之间的差距，我们提出一种结合教师-学生蒸馏与GRPO的新型多任务训练流水线，通过直接优化下游搜索满意度来对齐模型与复杂用户偏好。该方案在中国最大的自营电商平台上经过严格离线评估与在线A/B测试验证，显示出显著改进。具体而言，在线结果揭示分面点击率（CTR）相对提升42.0%，用户转化率（UCVR）提升2.0%。这些结果为生成式方法在提升大规模信息检索系统中查询理解与用户参与度方面的优势提供了有力证据。