25/07/2020

Towards personalized and semantic retrieval: An end-to-end solution for e-commerce search via embedding learning

Han Zhang, Songlin Wang, Kang Zhang, Zhiling Tang, Yunjiang Jiang, Yun Xiao, Weipeng Yan, Wen-Yun Yang

Keywords: semantic matching, search, neural networks

Abstract Paper Similar Papers

Abstract: 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

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