research-article

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Authors:

Nguyen Quoc Viet Hung,

Xiangliang ZhangAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 413 - 424

https://doi.org/10.1145/3442381.3449844

Published: 03 June 2021 Publication History

Abstract

Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences. However, real-life interactions among users are very complex and user relations can be high-order. Hypergraph provides a natural way to model high-order relations, while its potentials for improving social recommendation are under-explored. In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations. Technically, each channel in the network encodes a hypergraph that depicts a common high-order user relation pattern via hypergraph convolution. By aggregating the embeddings learned through multiple channels, we obtain comprehensive user representations to generate recommendation results. However, the aggregation operation might also obscure the inherent characteristics of different types of high-order connectivity information. To compensate for the aggregating loss, we innovatively integrate self-supervised learning into the training of the hypergraph convolutional network to regain the connectivity information with hierarchical mutual information maximization. Extensive experiments on multiple real-world datasets demonstrate the superiority of the proposed model over the current SOTA methods, and the ablation study verifies the effectiveness and rationale of the multi-channel setting and the self-supervised task. The implementation of our model is available via https://github.com/Coder-Yu/RecQ.

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cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

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Google
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Jie Tang
Tsinghua University
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Published: 03 June 2021

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Duan ZWang CZhong W(2024)SSGCL: Simple Social Recommendation with Graph Contrastive LearningMathematics10.3390/math1207110712:7(1107)Online publication date: 7-Apr-2024
https://doi.org/10.3390/math12071107
Li MLi JYang LDing Q(2024)Self-Supervised Hypergraph Learning for Knowledge-Aware Social RecommendationElectronics10.3390/electronics1307130613:7(1306)Online publication date: 31-Mar-2024
https://doi.org/10.3390/electronics13071306
Jiang YGao YSun YWang SYan C(2024)Multi-Channel Hypergraph Collaborative Filtering with Attribute InferenceElectronics10.3390/electronics1305090313:5(903)Online publication date: 27-Feb-2024
https://doi.org/10.3390/electronics13050903
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