research-article

Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems

Authors:

Chin-Chia Michael Yeh,

Hao YangAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 614 - 623

https://doi.org/10.1145/3404835.3462868

Published: 11 July 2021 Publication History

Abstract

Graph Convolutional Networks (GCNs) are powerful for collaborative filtering. The key component of GCNs is to explore neighborhood aggregation mechanisms to extract high-level representations of users and items. However, real-world user-item graphs are often incomplete and noisy. Aggregating misleading neighborhood information may lead to sub-optimal performance if GCNs are not regularized properly. Also, the real-world user-item graphs are often sparse and low rank. These two intrinsic graph properties are widely used in shallow matrix completion models, but far less studied in graph neural models. Here we propose Structured Graph Convolutional Networks (SGCNs) to enhance the performance of GCNs by exploiting graph structural properties of sparsity and low rank. To achieve sparsity, we attach each layer of a GCN with a trainable stochastic binary mask to prune noisy and insignificant edges, resulting in a clean and sparsified graph. To preserve its low-rank property, the nuclear norm regularization is applied. We jointly learn the parameters of stochastic binary masks and original GCNs by solving a stochastic binary optimization problem. An unbiased gradient estimator is further proposed to better backpropagate the gradients of binary variables. Experimental results demonstrate that SGCNs achieve better performance compared with the state-of-the-art GCNs.

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Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems

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Cited By

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https://doi.org/10.1155/2024/2978680
Ma JBian KXu YZhu L(2024)ANAGL: A Noise-resistant and Anti-sparse Graph Learning for micro-video recommendationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3670407Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670407
Luo TLiu YPan S(2024)Collaborative Sequential Recommendations via Multi-view GNN-transformersACM Transactions on Information Systems10.1145/364943642:6(1-27)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3649436
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Index Terms

Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

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Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

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Cited By

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https://doi.org/10.1155/2024/2978680
Ma JBian KXu YZhu L(2024)ANAGL: A Noise-resistant and Anti-sparse Graph Learning for micro-video recommendationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3670407Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670407
Luo TLiu YPan S(2024)Collaborative Sequential Recommendations via Multi-view GNN-transformersACM Transactions on Information Systems10.1145/364943642:6(1-27)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3649436
Chen HLai VJin HJiang ZDas MHu XAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Towards Mitigating Dimensional Collapse of Representations in Collaborative FilteringProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635832(106-115)Online publication date: 4-Mar-2024
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