Exploiting social networks is expected to enhance the performance of recommender
systems when interaction information is sparse. Existing social recommendation models …

Recommender systems usually encounter the issue of sparse interaction data, which is
commonly alleviated by social recommendation models based on graph neural networks …

Contrastive learning (CL) has recently catalyzed a productive avenue of research for
recommendation. The efficacy of most CL methods for recommendation may hinge on their …

Owing to the strength in learning representation of the high-order connectivity of graph
neural networks (GNN), GNN-based collaborative filtering has been widely adopted in …

Social recommendation based on social network has achieved great success in improving
the performance of the recommendation system. Since social network (user-user relations) …

Due to the strong representation capabilities of graph structures in social networks, social
relationships are often used to improve recommendation quality. Most existing social …

As an effective deep representation learning technique for graph data, graph convolutional
network (GCN) has recently been widely applied to obtain better embedding of vertex. The …

Most graph convolutional network (GCN)-based social recommendation frameworks fuse
social links with user-item interactions to enrich user representations, which alleviate the …

Traditional collaborative filtering based recommender systems generally suffer from the
interaction data sparsity problem. Therefore, social recommendation is proposed to mitigate …

Graph Neural Networks (GNNs) have emerged as an effective approach for social
recommender systems. GNNs excel at capturing the graph-structured semantic information …