沃尔玛电商搜索融合传统与神经检索，提升长尾查询相关性。
Semantic Retrieval at Walmart
- 结合倒排索引与嵌入式神经检索的混合系统
- 显著提升长尾查询的搜索相关性，线上线下评估均验证
- 可生产部署，响应延迟几乎无增加，适合电商搜索场景
在商品搜索中，重排序前的候选商品召回比网络搜索更关键且更具挑战性，尤其针对长尾查询——这类查询具有复杂且特定的搜索意图。本文介绍沃尔玛部署的混合搜索系统，结合传统的倒排索引与基于嵌入的神经检索，以更好应对用户长尾查询。该系统在离线和在线评估中均显著提升了搜索相关性。改进得益于多种方法的组合：提出一种可扩展训练神经模型的新技术，并描述了系统如何在对响应时间影响极小的情况下投入生产。文中还总结了多项实践经验与实用技巧，助力系统落地。
原文摘要 · Abstract (English)
In product search, the retrieval of candidate products before re-ranking is more critical and challenging than other search like web search, especially for tail queries, which have a complex and specific search intent. In this paper, we present a hybrid system for e-commerce search deployed at Walmart that combines traditional inverted index and embedding-based neural retrieval to better answer user tail queries. Our system significantly improved the relevance of the search engine, measured by both offline and online evaluations. The improvements were achieved through a combination of different approaches. We present a new technique to train the neural model at scale. and describe how the system was deployed in production with little impact on response time. We highlight multiple learnings and practical tricks that were used in the deployment of this system.
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