We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the design of a deep learning system that retrieves semantically relevant items to a query within milliseconds, and a pairwise deep re-ranking system, which learns subtle user preferences. Compared to traditional search systems, the proposed approaches are better at semantic retrieval and personalized ranking, achieving significant improvements.
@article{arxiv.2103.12982,
title = {From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search},
author = {Rui Li and Yunjiang Jiang and Wenyun Yang and Guoyu Tang and Songlin Wang and Chaoyi Ma and Wei He and Xi Xiong and Yun Xiao and Eric Yihong Zhao},
journal= {arXiv preprint arXiv:2103.12982},
year = {2021}
}