用生成式模型提升电商搜索的智能导航，让推荐更懂用户意图。
GenFacet: End-to-End Generative Faceted Search via Multi-Task Preference Alignment in E-Commerce
- 将筛选项生成与查询改写联合建模，实现端到端动态推荐。
- 线上测试显示点击率提升42.0%，转化率提高2.0%。
- 适合关注搜索体验优化与大模型应用的工业界研究者。
传统电商筛选系统依赖静态规则或统计排序，难以应对新词、语义鸿沟及筛选与检索脱节问题。本文提出GenFacet，一个在京东大规模部署的端到端生成式框架。该框架将筛选项生成与用户意图驱动的查询重写视为统一大模型中的两个耦合生成任务：前者动态生成趋势响应的导航选项，后者将用户行为转化为精准搜索词以闭合检索闭环。为对齐生成能力与搜索效用，提出融合教师-学生蒸馏与GRPO的多任务训练流程，直接优化下游搜索满意度。在最大自营电商平台的离线评估与线上A/B测试中验证，线上结果表明筛选项点击率相对提升42.0%，用户转化率提升2.0%。结果证明生成式方法能显著提升大规模信息检索中的查询理解与用户参与度。
原文摘要 · Abstract (English)
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.
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