research-article

Learning Shared Representations for Recommendation with Dynamic Heterogeneous Graph Convolutional Networks

Authors:

Jiadi YuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 4

Article No.: 59, Pages 1 - 23

https://doi.org/10.1145/3565575

Published: 24 February 2023 Publication History

Abstract

Graph Convolutional Networks (GCNs) have been widely used for collaborative filtering, due to their effectiveness in exploiting high-order collaborative signals. However, two issues have not been well addressed by existing studies. First, usually only one kind of information is utilized, i.e., user preference in user-item graphs or item dependency in item-item graphs. Second, they usually adopt static graphs, which cannot retain the temporal evolution of the information. These can limit the recommendation quality. To address these limitations, we propose to mine three kinds of information (user preference, item dependency, and user behavior similarity) and their temporal evolution by constructing multiple discrete dynamic heterogeneous graphs (i.e., a user-item dynamic graph, an item-item dynamic graph, and a user-subseq dynamic graph) from interaction data. A novel network (PDGCN) is proposed to learn the representations of users and items in these dynamic graphs. Moreover, we designed a structural neighbor aggregation module with novel pooling and convolution operations to aggregate the features of structural neighbors. We also design a temporal neighbor aggregation module based on self-attention mechanism to aggregate the features of temporal neighbors. We conduct extensive experiments on four real-world datasets. The results indicate that our approach outperforms several competing methods in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Dynamic graphs are also shown to be effective in improving recommendation performance.

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Index Terms

Learning Shared Representations for Recommendation with Dynamic Heterogeneous Graph Convolutional Networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 4

May 2023

364 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3583065

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 24 February 2023

Online AM: 10 October 2022

Accepted: 16 September 2022

Revised: 27 May 2022

Received: 02 September 2021

Published in TKDD Volume 17, Issue 4

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2030 National Key AI Program of China
National Science Foundation of China
Shanghai Municipal Science and Technology Commission
Oceanic Interdisciplinary Program of Shanghai Jiao Tong University
Scientific Research Fund of Second Institute of Oceanography

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Cited By

Yu HZheng ZXue YSong YLiang Z(2025)Knowledge based attribute completion for heterogeneous graph node classificationNeurocomputing10.1016/j.neucom.2024.129023619(129023)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129023
Cui FYu SChai YQian YJiang YLiu YLiu XLi J(2024)A Bayesian deep recommender system for uncertainty-aware online physician recommendationInformation and Management10.1016/j.im.2024.10402761:7Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.im.2024.104027
Liu HZhang H(2023)MDBF: Meta-Path-Based Depth and Breadth Feature Fusion for Recommendation in Heterogeneous NetworkElectronics10.3390/electronics1219401712:19(4017)Online publication date: 24-Sep-2023
https://doi.org/10.3390/electronics12194017
Li YZhao FChen ZFu YMa L(2023)Multi-Behavior Enhanced Heterogeneous Graph Convolutional Networks Recommendation Algorithm based on Feature-InteractionApplied Artificial Intelligence10.1080/08839514.2023.220114437:1Online publication date: 12-Apr-2023
https://doi.org/10.1080/08839514.2023.2201144

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