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arXiv · 2103.12982

From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search

Rui Li·Yunjiang Jiang·Wenyun Yang·Guoyu Tang·Songlin Wang·Chaoyi Ma·Wei He·Xi Xiong·Yun Xiao·Eric Yihong Zhao
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Abstract

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.

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Rui Li, Yunjiang Jiang, Wenyun Yang, Guoyu Tang, Songlin Wang, Chaoyi Ma, Wei He, Xi Xiong, Yun Xiao, Eric Yihong Zhao. 2021-03-24. From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search. https://arxiv.org/abs/2103.12982

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