论文标题
迈向个性化和语义检索：通过嵌入学习的电子商务搜索的端到端解决方案
Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
论文作者
论文摘要
如今，电子商务搜索已成为许多人购物例程中不可或缺的一部分。在当今的电子商务搜索中留下了两个关键的挑战：如何检索语义上相关但与查询术语不完全匹配的项目，以及如何检索与其他用户更具个性化的项目以获取相同的搜索查询。在本文中，我们提出了一种名为DPSR的新颖方法，该方法代表着深刻的个性化和语义检索，以解决这个问题。明确地，我们分享了有关如何构建检索系统的设计决策，以便有效地服务行业规模的流量以及如何培训模型，以便准确学习查询和项目语义。基于离线评估和与现场贩运的在线A/B测试，我们表明DPSR模型的表现优于现有模型，而DPSR系统可以检索更多个性化和语义相关的项目，以将用户的搜索体验显着提高 +1.29％的转换率，尤其是对于长时间的尾部查询而言，尤其是 +10.03％。结果，自2019年以来，我们的DPSR系统已成功部署到JD.com的搜索生产中。
Nowadays e-commerce search has become an integral part of many people's shopping routines. Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query terms, and how to retrieve items that are more personalized to different users for the same search query. In this paper, we present a novel approach called DPSR, which stands for Deep Personalized and Semantic Retrieval, to tackle this problem. Explicitly, we share our design decisions on how to architect a retrieval system so as to serve industry-scale traffic efficiently and how to train a model so as to learn query and item semantics accurately. Based on offline evaluations and online A/B test with live traffics, we show that DPSR model outperforms existing models, and DPSR system can retrieve more personalized and semantically relevant items to significantly improve users' search experience by +1.29% conversion rate, especially for long tail queries by +10.03%. As a result, our DPSR system has been successfully deployed into JD.com's search production since 2019.
