Social recommender systems are expected to improve recommendation quality by
incorporating social information when there is little user-item interaction data. However …

Recommender systems in social networks are widely used for connecting users to their
desired items from a vast catalog of available items. Learning the user's preferences from all …

Social recommendation, which leverages social connections to construct Recommender
Systems (RS), plays an important role in alleviating information overload. Recently, Graph …

Recent years have witnessed rapid developments on social recommendation techniques for
improving the performance of recommender systems due to the growing influence of social …

Collaborative Filtering (CF) is one of the most successful approaches for recommender
systems. With the emergence of online social networks, social recommendation has become …

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node
information and topological structure, have been demonstrated to be powerful in learning on …

Social recommendation leverages social information to solve data sparsity and cold-start
problems in traditional collaborative filtering methods. However, most existing models …

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 …

Social recommendation task aims to predict users' preferences over items with the
incorporation of social connections among users, so as to alleviate the sparse issue of …

Social relations are often used as auxiliary information to improve recommendations. In the
real-world, social relations among users are complex and diverse. However, most existing …