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Nowadays e-commerce search has become an integral part of many people'sshopping routines. Two critical challenges stay in today's e-commerce search:how to retrieve items that are semantically relevant but not exact matching toquery terms, and how to retrieve items that are more personalized to differentusers for the same search query. In this paper, we present a novel approachcalled DPSR, which stands for Deep Personalized and Semantic Retrieval, totackle this problem. Explicitly, we share our design decisions on how toarchitect a retrieval system so as to serve industry-scale traffic efficientlyand 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 showthat DPSR model outperforms existing models, and DPSR system can retrieve morepersonalized and semantically relevant items to significantly improve users'search experience by +1.29% conversion rate, especially for long tail queriesby +10.03%. As a result, our DPSR system has been successfully deployed intoJD.com's search production since 2019.

Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning