  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.
