Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model
2023-07-01
状态已发表
摘要

Discovering the intended items of user queries from a massive repository of items is one of the main goals of an e-commerce search system. Relevance prediction is essential to the search system since it helps improve performance. When online serving a relevance model, the model is required to perform fast and accurate inference. Currently, the widely used models such as Bi-encoder and Cross-encoder have their limitations in accuracy or inference speed respectively. In this work, we propose a novel model called the Entity-Based Relevance Model (EBRM). We identify the entities contained in an item and decompose the QI (query-item) relevance problem into multiple QE (query-entity) relevance problems; we then aggregate their results to form the QI prediction using a soft logic formulation. The decomposition allows us to use a Cross-encoder QE relevance module for high accuracy as well as cache QE predictions for fast online inference. Utilizing soft logic makes the prediction procedure interpretable and intervenable. We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance. The proposed method is evaluated on labeled data from e-commerce websites. Empirical results show that it achieves promising improvements with computation efficiency.

DOIarXiv:2307.00370
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出处Arxiv
WOS记录号PPRN:74074798
WOS类目Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
文献类型预印本
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348097
专题信息科学与技术学院
信息科学与技术学院_PI研究组_屠可伟组
信息科学与技术学院_博士生
作者单位
1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
2.Alibaba Grp, DAMO Acad, Hangzhou, Peoples R China
推荐引用方式
GB/T 7714
Cai, Jiong,Jiang, Yong,Zhang, Yue,et al. Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model. 2023.
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