Query Rewriting via Cycle-Consistent Translation for E-Commerce Search
Yiming Qiu, Kang Zhang, Han Zhang, Songlin Wang, Sulong Xu, Yun Xiao,, Bo Long, Wen-Yun Yang

TL;DR
This paper introduces a cycle-consistent neural network approach for query rewriting in e-commerce search, improving semantic matching and retrieval accuracy by leveraging click log data and optimizing for industrial deployment.
Contribution
It formulates query rewriting as a cyclic translation problem and develops a novel training algorithm, significantly enhancing diversity and relevance over rule-based methods.
Findings
Improved query rewriting accuracy and diversity.
Significant online business metric improvements.
Deployed at scale serving hundreds of millions of users.
Abstract
Nowadays e-commerce search has become an integral part of many people's shopping routines. One critical challenge in today's e-commerce search is the semantic matching problem where the relevant items may not contain the exact terms in the user query. In this paper, we propose a novel deep neural network based approach to query rewriting, in order to tackle this problem. Specifically, we formulate query rewriting into a cyclic machine translation problem to leverage abundant click log data. Then we introduce a novel cyclic consistent training algorithm in conjunction with state-of-the-art machine translation models to achieve the optimal performance in terms of query rewriting accuracy. In order to make it practical in industrial scenarios, we optimize the syntax tree construction to reduce computational cost and online serving latency. Offline experiments show that the proposed method…
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