发表机构
Target Corporation（塔吉特公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍了塔吉特公司结合词汇与向量搜索的混合商品搜索系统，通过数据处理、嵌入训练、精度控制及加权交错融合等优化，显著提升点击率、转化率与需求，并大幅减少零结果搜索。
AI 中文摘要
搜索是电子商务中最重要的功能之一，直接推动客户参与和业务增长。一个好的商品搜索系统必须同时展示相关且令人满意的结果。然而，零售搜索面临独特的挑战。用户意图可能从精确匹配到开放式发现不等。搜索系统还必须在保持低响应时间的同时，平衡相关性、收入和利润等多重目标。传统的基于关键词的方法在处理自然语言或语义查询时往往力不从心。向量搜索有助于缓解这些问题，但可能错过关键的意图信号或返回低精度的结果。在本文中，我们介绍了塔吉特公司一个结合词汇搜索和向量搜索的混合搜索系统的设计。我们描述了在数据处理、嵌入训练、最终结果集的精度控制、多通道结果融合（其中我们比较了融合策略并采用了加权交错）以及用于维持生产部署低延迟的性能优化方面的做法。我们的方法提高了离线评估指标，在在线A/B测试中，与仅词汇搜索相比，点击率提高了0.97%，订单转化率提高了0.98%，每位访客的需求量提高了1.10%，同时零结果搜索大约减少了一半。由此产生的系统已大规模部署，每天服务数百万顾客。
英文摘要
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.
Comments10 pages, 2 figures, 6 tables
