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

TL;DR

This paper introduces a multi-channel hypergraph convolutional network with self-supervised learning to better model high-order user relations for improved social recommendation, outperforming state-of-the-art methods.

Contribution

It proposes a novel multi-channel hypergraph convolutional network combined with self-supervised learning to capture complex high-order user relations in social recommendation.

Findings

Outperforms state-of-the-art social recommendation methods.

Multi-channel hypergraph encoding improves user representation.

Self-supervised learning enhances connectivity information recovery.

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.