Self-supervised-Enhanced Dual Hierarchical Graph Convolution Network for Social Recommendation

Yixing Guo, Weimin Li, Jingchao Wang, Shaohua Li

Abstract: Graph convolution networks (GCNs) have made significant progress in the field of recommendation systems in recent years, and many GCN-based frameworks have applied in social recommendation methods. The essence of social recommendation tasks is modeling user preferences through user social relationships to alleviate the sparsity issue. However, existing GCN-based social recommendation frameworks still have some inherent problems. Firstly, since there are no node attributes available as semantic information in the recommendation task, lightweight graph convolutions that remove feature transformation and non-linear activation function have become widely applied in recommendation task, with the core lies in its message passing mechanism. Social recommendation methods always directly apply existing message passing paradigms, which always have obvious limitations in their message passing mechanisms. Secondly, most existing social recommendation frameworks are limited to pairwise relations and unable to effectively extract implicit inter-graph information. To address these issues, we propose a Self-Supervised-Enhanced Dual Hierarchical Graph Convolution Network (SSHGCN). In this framework, we first propose a LEWB (Link Encoded-Weight Balanced) message passing paradigm applied to graph convolution network to train user and item representations. Then we explicitly model marginal information between user social graph and user-item interaction graph, item knowledge graph and user-item interactions graph by hypergraph. Finally, we construct hierarchical self-supervised signals and unify self-supervised task and recommendation task for joint training. Extensive experiments on real-world datasets demonstrate that our method outperforms competitive methods. Thorough ablation study verifies the rationality of LEWB message passing paradigm and the effectiveness of the hierarchical self-supervised tasks in our framework.