research-article

Bilateral Filtering Graph Convolutional Network for Multi-relational Social Recommendation in the Power-law Networks

Authors:

Peng CuiAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 2

Article No.: 31, Pages 1 - 24

https://doi.org/10.1145/3469799

Published: 27 September 2021 Publication History

Abstract

In recent years, advances in Graph Convolutional Networks (GCNs) have given new insights into the development of social recommendation. However, many existing GCN-based social recommendation methods often directly apply GCN to capture user-item and user-user interactions, which probably have two main limitations: (a) Due to the power-law property of the degree distribution, the vanilla GCN with static normalized adjacency matrix has limitations in learning node representations, especially for the long-tail nodes; (b) multi-typed social relationships between users that are ubiquitous in the real world are rarely considered. In this article, we propose a novel Bilateral Filtering Heterogeneous Attention Network (BFHAN), which improves long-tail node representations and leverages multi-typed social relationships between user nodes. First, we propose a novel graph convolutional filter for the user-item bipartite network and extend it to the user-user homogeneous network. Further, we theoretically analyze the correlation between the convergence values of different graph convolutional filters and node degrees after stacking multiple layers. Second, we model multi-relational social interactions between users as the multiplex network and further propose a multiplex attention network to capture distinctive inter-layer influences for user representations. Last but not least, the experimental results demonstrate that our proposed method outperforms several state-of-the-art GCN-based methods for social recommendation tasks.

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Cited By

Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3661821
Zhu YYi JXie JChen Z(2024)Deep Causal Reasoning for RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/365398515:4(1-25)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3653985
Yu MZhu KZhao MYu JXu TJin DLi XYu R(2024)Learning Neighbor User Intention on User–Item Interaction Graphs for Better Sequential RecommendationACM Transactions on the Web10.1145/358052018:2(1-28)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3580520
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Index Terms

Bilateral Filtering Graph Convolutional Network for Multi-relational Social Recommendation in the Power-law Networks
1. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 2

April 2022

587 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3484931

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 27 September 2021

Accepted: 01 June 2021

Revised: 01 April 2021

Received: 01 November 2020

Published in TOIS Volume 40, Issue 2

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Cited By

Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3661821
Zhu YYi JXie JChen Z(2024)Deep Causal Reasoning for RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/365398515:4(1-25)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3653985
Yu MZhu KZhao MYu JXu TJin DLi XYu R(2024)Learning Neighbor User Intention on User–Item Interaction Graphs for Better Sequential RecommendationACM Transactions on the Web10.1145/358052018:2(1-28)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3580520
Dai JLi QNong TBi QChu H(2024)Joint contrastive learning of structural and semantic for graph collaborative filteringNeurocomputing10.1016/j.neucom.2024.127547586(127547)Online publication date: Jun-2024
https://doi.org/10.1016/j.neucom.2024.127547
Ni XXiong FPan SWu JWang LChen H(2023)Community Preserving Social Recommendation with Cyclic Transfer LearningACM Transactions on Information Systems10.1145/363111542:3(1-36)Online publication date: 29-Dec-2023
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Yan MCheng ZGao CSun JLiu FSun FLi H(2023)Cascading Residual Graph Convolutional Network for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/358769342:1(1-26)Online publication date: 18-Aug-2023
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