short-paper

A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval

Authors:

Sulong XuAuthors Info & Claims

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

Pages 416 - 420

https://doi.org/10.1145/3543873.3584639

Published: 30 April 2023 Publication History

Abstract

Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisite models to enhance the representation learning of queries or explore label-graph and multi-task to facilitate models to learn external information. However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories.

This paper proposes a Multi-granularity Matching Attention Network (MMAN), which contains three modules: a self-matching module, a char-level matching module, and a semantic-level matching module to comprehensively extract features from the query and a query-category interaction matrix. In this way, the model can eliminate the difference in expression between queries and categories for query intent classification. We conduct extensive offline and online A/B experiments, and the results show that the MMAN significantly outperforms the strong baselines, which shows the superiority and effectiveness of MMAN. MMAN has been deployed in production and brings great commercial value for our company.

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Cited By

Yuan CPang MFang ZJiang XPeng CLin ZChua TNgo CKumar RLauw HKa-Wei Lee R(2024)A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerceCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648302(56-64)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648302
Satwik Reddy NRohith VAbhiram PSaran MRebecca S(2024)Enhancing Product Categorization in E-commerce using NLP and Machine Learning2024 International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT60155.2024.10544665(1-6)Online publication date: 24-Apr-2024
https://doi.org/10.1109/ICICT60155.2024.10544665
Zhu LZhang KChen HWei CZhang WTang HLi XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)HCL4QC: Incorporating Hierarchical Category Structures Into Contrastive Learning for E-commerce Query ClassificationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614907(3647-3656)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614907
Show More Cited By

Index Terms

A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
      1. Query intent

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cover image ACM Conferences

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

April 2023

1567 pages

ISBN:9781450394192

DOI:10.1145/3543873

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 30 April 2023

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WWW '23

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SIGWEB

WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Yuan CPang MFang ZJiang XPeng CLin ZChua TNgo CKumar RLauw HKa-Wei Lee R(2024)A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerceCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648302(56-64)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648302
Satwik Reddy NRohith VAbhiram PSaran MRebecca S(2024)Enhancing Product Categorization in E-commerce using NLP and Machine Learning2024 International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT60155.2024.10544665(1-6)Online publication date: 24-Apr-2024
https://doi.org/10.1109/ICICT60155.2024.10544665
Zhu LZhang KChen HWei CZhang WTang HLi XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)HCL4QC: Incorporating Hierarchical Category Structures Into Contrastive Learning for E-commerce Query ClassificationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614907(3647-3656)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614907
Wang MLiu JWang JWang YChu X(2023)A Topicality Relevance-Aware Intent Model for Web SearchIEEE Access10.1109/ACCESS.2023.328982011(65739-65748)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3289820

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