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

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    Type
    Preprint
    Authors
    Yu, Junliang
    Yin, Hongzhi
    Li, Jundong
    Wang, Qinyong
    Hung, Nguyen Quoc Viet
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2021-01-16
    Permanent link to this record
    http://hdl.handle.net/10754/668790
    
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    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 complicated and user relations can be high-order. Hypergraph provides a natural way to model complex 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. The experimental results on multiple real-world datasets show that the proposed model outperforms the SOTA methods, and the ablation study verifies the effectiveness of the multi-channel setting and the self-supervised task. The implementation of our model is available via https://github.com/Coder-Yu/RecQ.
    Sponsors
    This work was supported by ARC Discovery Project (Grant No. DP190101985 and DP170103954). Jundong Li is supported by National Science Foundation (NSF) under grant No. 2006844.
    Publisher
    arXiv
    arXiv
    2101.06448
    Additional Links
    https://arxiv.org/pdf/2101.06448.pdf
    Collections
    Preprints; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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