AI 中文总结
该研究针对电商搜索中改写与检索错位问题，提出SPEAR框架，通过三个组件消除通用词捷径，离线与在线实验均验证其能提升搜索效果，已部署于得物社区搜索平台。
AI 中文摘要
查询改写在电商搜索中连接用户意图与检索，但生产系统分别优化改写质量与检索效果，导致两阶段结构错位。基于路径的架构虽将二者端到端统一，但并非为个性化设计——个性化中相关性并非显式约束，而搜索还要求改写忠实于用户明确的查询意图。直接迁移这类模型会学习到一种被称为“通用词主导效应”的捷径：它们倾向于在路径上得分高但偏离查询意图的通用改写。为解决该问题，我们提出SPEAR（Selection-aware Personalized End-to-end Adaptive Rewriting and Retrieval，选择感知个性化端到端自适应改写与检索），其包含三个组件分别针对一种失效模式：（1）带辅助损失与梯度隔离的双嵌入骨干，保护召回侧语义不被CTR驱动的排序信号侵蚀；（2）乘法门控聚合器，仅当改写的置信度与物品相关性均较强时才使其得分高，消除通用词捷径；（3）动态改写选择器，联合生成请求特定的改写权重及用户-查询条件下的缩放与偏置项，使改写偏好与相关性校准均能适配每个请求。对10万条保留的工业搜索会话的离线评估显示，该框架较生产基线使改写语义相似度@10提升18.2，点击召回率@10提升99.5。在线A/B测试中，SPEAR实现查询视图CTR提升0.259，平均阅读深度提升0.733，证实改进的改写选择可转化为更强的检索效果与更深的用户参与度。所提出的SPEAR系统已于2025年全量部署于得物的社区搜索平台，代码可在指定网址获取。
英文摘要
Query reformulation bridges user intent and retrieval in e-commerce search, yet production systems optimize rewrite quality and retrieval effectiveness separately, leaving the two stages structurally misaligned. Path-based architectures unify them end-to-end but were designed for personalization, where relevance is not an explicit constraint-search additionally requires the rewrite to remain faithful to the user's stated query intent. Transplanted directly, these models learn a shortcut we term the generic-word dominance effect: they favor generic rewrites that score well on paths but drift from query intent. To address this, we propose SPEAR (Selection-aware Personalized End-to-end Adaptive Rewriting and Retrieval), which integrates three components that each target one failure mode: (1) a dual-embedding backbone with auxiliary loss and gradient isolation that shields recall-side semantics from being eroded by CTR-driven ranking signals; (2) a multiplicative gating aggregator that lets a rewrite score high only when both its confidence and item relevance are strong, eliminating the generic-word shortcut; (3) a Dynamic Rewrite Selector that jointly generates request-specific rewrite weights and user-query-conditioned scale and bias terms, allowing both rewrite preference and relevance calibration to adapt to each request. Offline evaluation on 100K held-out industrial search sessions shows that the proposed framework improves rewrite semantic similarity@10 by +18.2 and click recall@10 by +99.5 over the production baseline. In online A/B testing, SPEAR achieves +0.259 in query-view CTR and +0.733 in average reading depth, confirming that improved rewrite selection translates into stronger retrieval and deeper user engagement. The proposed SPEAR system has been fully deployed in Dewu's community search platform since 2025. Our code is available at https://github.com/mallocagi1-cell/spear.
Comments11 pages, 5 figures, 5 tables. Accepted to the Main Track of the 20th ACM Conference on Recommender Systems (RecSys 2026). Code: https://github.com/mallocagi1-cell/spear
Journal refProceedings of the 20th ACM Conference on Recommender Systems (RecSys '26), September 27-October 2, 2026, Minneapolis, MN, USA, 11 pages
