research-article

Hierarchical User Profiling for E-commerce Recommender Systems

Authors:

Shuaiqiang Wang,

Dawei YinAuthors Info & Claims

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

Pages 223 - 231

https://doi.org/10.1145/3336191.3371827

Published: 22 January 2020 Publication History

Abstract

Hierarchical user profiling that aims to model users' real-time interests in different granularity is an essential issue for personalized recommendations in E-commerce. On one hand, items (i.e. products) are usually organized hierarchically in categories, and correspondingly users' interests are naturally hierarchical on different granularity of items and categories. On the other hand, multiple granularity oriented recommendations become very popular in E-commerce sites, which require hierarchical user profiling in different granularity as well. In this paper, we propose HUP, a Hierarchical User Profiling framework to solve the hierarchical user profiling problem in E-commerce recommender systems. In HUP, we provide a Pyramid Recurrent Neural Networks, equipped with Behavior-LSTM to formulate users' hierarchical real-time interests at multiple scales. Furthermore, instead of simply utilizing users' item-level behaviors (e.g., ratings or clicks) in conventional methods, HUP harvests the sequential information of users' temporal finely-granular interactions (micro-behaviors, e.g., clicks on components of items like pictures or comments, browses with navigation of the search engines or recommendations) for modeling. Extensive experiments on two real-world E-commerce datasets demonstrate the significant performance gains of the HUP against state-of-the-art methods for the hierarchical user profiling and recommendation problems. We release the codes and datasets at https://github.com/guyulongcs/WSDM2020_HUP.

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Index Terms

Hierarchical User Profiling for E-commerce Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization

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cover image ACM Conferences

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

January 2020

950 pages

ISBN:9781450368223

DOI:10.1145/3336191

General Chairs:
James Caverlee
Texas A&M University
,
Xia "Ben" Hu
Texas A&M University
,
Program Chairs:
Mounia Lalmas
Spotify
,
Wei Wang
University of California, Los Angeles

Copyright © 2020 ACM.

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Published: 22 January 2020

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WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining

February 3 - 7, 2020

TX, Houston, USA

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Cited By

Celik EIlhan Omurca S(2024)Skip-Gram and Transformer Model for Session-Based RecommendationApplied Sciences10.3390/app1414635314:14(6353)Online publication date: 21-Jul-2024
https://doi.org/10.3390/app14146353
Lin LXu XWang HHe TZhang DWang SSerra ESpezzano F(2024)DIFN: A Dual Intention-aware Network for Repurchase Recommendation with Hierarchical Spatio-temporal FusionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680071(4710-4717)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680071
Wu CShi SWang CLiu ZPeng WWu WKong DLi HGai KChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Enhancing Recommendation Accuracy and Diversity with Box Embedding: A Universal FrameworkProceedings of the ACM Web Conference 202410.1145/3589334.3645577(3756-3766)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645577
He WXiao YLi TWang RLi Q(2024)Interest HD: An Interest Frame Model for Recommendation Based on HD Image GenerationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.327867335:10(14356-14369)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3278673
Ren ZWan KZhang RQiao P(2024)Digital Twin Portrait: A Fusion and Application Method of Multisource Twin Data for Flexible Manufacturing LineIEEE Journal of Emerging and Selected Topics in Industrial Electronics10.1109/JESTIE.2023.33177925:2(753-762)Online publication date: Apr-2024
https://doi.org/10.1109/JESTIE.2023.3317792
Li FLi HDong R(2024)PRDG: Personalized Recommendation with Diversity Based on Graph Neural Networks2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651479(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651479
Zou GWu LHu SYang SZhou CGan YZhang B(2024)User Profiling for Personalized Service Recommendation with Dual High-order Feature Learning2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00049(269-280)Online publication date: 7-Jul-2024
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Zhu XZhang YWang JWang G(2024)Graph-enhanced and collaborative attention networks for session-based recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111509289(111509)Online publication date: Apr-2024
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Chen YCao QHuang XZou S(2024)Multi-behavior collaborative contrastive learning for sequential recommendationComplex & Intelligent Systems10.1007/s40747-024-01423-110:4(5033-5048)Online publication date: 15-Apr-2024
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Liu TZhou HLi CZhao Z(2024)Self-supervised progressive graph neural network for enhanced multi-behavior recommendationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02353-7Online publication date: 4-Sep-2024
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