  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.
