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.
@article{arxiv.2006.02282,
title = {Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning},
author = {Han Zhang and Songlin Wang and Kang Zhang and Zhiling Tang and Yunjiang Jiang and Yun Xiao and Weipeng Yan and Wen-Yun Yang},
journal= {arXiv preprint arXiv:2006.02282},
year = {2020}
}