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

• URL: http://arxiv.org/abs/2103.12982v1
• Date: Wed, 24 Mar 2021 04:37:32 GMT
• Title: From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search
• Authors: Rui Li, Yunjiang Jiang, Wenyun Yang, Guoyu Tang, Songlin Wang, Chaoyi Ma, Wei He, Xi Xiong, Yun Xiao, Eric Yihong Zhao
• Abstract summary: We introduce deep learning models to the two most important stages in product search at JD.com. 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.
• Score: 11.459020190110019
• License: http://creativecommons.org/licenses/by/4.0/
• 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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