Target 公开零售电商混合搜索系统设计：词法检索结合向量搜索
Retail Product Search: A Practical Approach at Target
Target 公开了自家电商搜索的完整架构，从 embedding 训练到融合策略都讲了，做电商搜索的直接抄作业。
Target 在 arXiv 论文（编号 2609.31498）中介绍其电商搜索系统如何将词法检索与向量搜索结合。文章覆盖数据处理、embedding 训练、结果精度控制与多路融合策略，最终采用加权交错（weighted interleaving）方案。线上 A/B 测试显示，相比纯词法搜索，点击率提升 0.97%，下单转化率提升 0.98%，人均需求提升 1.10%，零结果搜索数量约减少一半。该系统已规模化部署，每天服务数百万顾客。
Retail Product Search: A Practical Approach at Target
Search is one of the most important features in e-commerce, directly driving customer engagement and business growth. A good product search system must show both relevant and desirable results. However, retail search presents unique challenges. User intent can range from exact matches to open-ended discovery. Search systems must also balance multiple goals, such as relevance, revenue, and profit, while keeping response times low. Traditional keyword-based methods often fall short in handling natural language or semantic queries. Vector search helps alleviate these issues, but it can miss key intent signals or return low-precision results. In this paper, we present the design of a hybrid search system at Target that combines lexical and vector search. We describe our approach to data processing, embedding training, precision control for the final result set, multi-channel result fusion (where we compared fusion strategies and adopted weighted interleaving), and the performance optimizations used to maintain low latency for production deployment. Our method improves offline evaluation metrics, and in online A/B testing it raised click-through rate by 0.97%, order conversion by 0.98%, and demand per visitor by 1.10% over lexical-only search, while roughly halving zero-result searches. The resulting system is deployed at scale and serves millions of guests daily.
