Social relations are often used to improve recommendation quality and 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 potential for social recommendation is 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. 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.

中文翻译：

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用于社会推荐的自监督多通道超图卷积网络

社交关系通常用于提高推荐质量，大多数现有的社交推荐模型利用成对关系来挖掘潜在的用户偏好。但是，用户之间的实际交互非常复杂，并且用户关系可能是高级的。Hypergraph提供了一种自然的方式来对复杂的高阶关系进行建模，而其社交推荐的潜力尚未得到充分的挖掘。在本文中，我们填补了这一空白，并提出了一种多通道超图卷积网络，以通过利用高阶用户关系来增强社会推荐。从技术上讲，网络中的每个通道都对超图进行编码，该超图通过超图卷积来描述常见的高阶用户关系模式。通过汇总通过多种渠道获得的嵌入，我们获得全面的用户表示，以生成推荐结果。但是，聚合操作也可能会掩盖不同类型的高阶连接信息的固有特性。为了弥补累积的损失，我们将自我监督学习创新地集成到超图卷积网络的训练中，以重新获得连通性信息。在多个实际数据集上的实验结果表明，所提出的模型优于SOTA方法，而消融研究证明了多通道设置和自我监督任务的有效性。我们将自我监督学习创新地集成到超图卷积网络的训练中，以重新获得连通性信息。在多个实际数据集上的实验结果表明，所提出的模型优于SOTA方法，而消融研究证明了多通道设置和自我监督任务的有效性。我们将自我监督学习创新地集成到超图卷积网络的训练中，以重新获得连通性信息。在多个实际数据集上的实验结果表明，所提出的模型优于SOTA方法，而消融研究证明了多通道设置和自我监督任务的有效性。