Social Recommendation Algorithm Based on Self-Supervised Hypergraph Attention

Abstract

Social network information has been widely applied to traditional recommendations that have received significant attention in recent years. Most existing social recommendation models tend to use pairwise relationships to explore potential user preferences, but overlook the complexity of real-life interactions between users and the fact that user relationships may be higher order. These approaches also ignore the dynamic nature of friend influence, which leads the models to treat different friend influences equally in different ways. To address this, we propose a social recommendation algorithm that incorporates graph embedding and higher-order mutual information maximization based on the consideration of social consistency. Specifically, we use the graph attention model to build higher-order information among users for deeper mining of their behavioral patterns on the one hand; while on the other hand, it models user embedding based on the principle of social consistency to finally achieve finer-grained inference of user interests. In addition, to alleviate the problem of losing its own hierarchical information after fusing different levels of hypergraphs, we use self-supervised learning to construct auxiliary branches that fully enhance the rich information in the hypergraph. Experimental results conducted on two publicly available datasets show that the proposed model outperforms state-of-the-art methods.

Social Recommendation Based on Contrastive Learning of Hypergraph Convolution

“…Xu et al [28] proposed a social recommendation algorithm based on social consistency, which combines graph embedding and high-order mutual information maximization and uses self-supervised learning to construct auxiliary branches to enhance the information richness in hypergraphs. However, in real scenarios, highorder information between users is often very complex.…”