Modeling User Behavior with Graph Convolution for Personalized Product Search

Lu Fan, Qimai Li, Bo Liu, Xiao Ming Wu, Xiaotong Zhang, Fuyu Lv, Guli Lin, Sen Li, Taiwei Jin, Keping Yang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance by jointly learning semantic representations of products, users, and text tokens. However, existing methods are limited in their ability to model user preferences. They typically represent users by the products they visited in a short span of time using attentive models and lack the ability to exploit relational information such as user-product interactions or item co-occurrence relations. In this work, we propose to address the limitations of prior arts by exploring local and global user behavior patterns on a user successive behavior graph, which is constructed by utilizing short-term actions of all users. To capture implicit user preference signals and collaborative patterns, we use an efficient jumping graph convolution to explore high-order relations to enrich product representations for user preference modeling. Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences. Extensive experiments on eight Amazon benchmarks demonstrate the effectiveness and potential of our approach. The source code is available at https://github.com/floatSDSDS/SBG .

Original languageEnglish
Title of host publicationWWW 2022 - Proceedings of the ACM Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Pages203-212
Number of pages10
ISBN (Electronic)9781450390965
DOIs
Publication statusPublished - 25 Apr 2022
Event31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France
Duration: 25 Apr 202229 Apr 2022

Publication series

NameWWW 2022 - Proceedings of the ACM Web Conference 2022

Conference

Conference31st ACM World Wide Web Conference, WWW 2022
Country/TerritoryFrance
CityVirtual, Online
Period25/04/2229/04/22

Keywords

  • Graph Convolution
  • Personalized Product Search
  • User Preference Modeling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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