Abstract Graph Neural Networks have been applied in recommender systems to learn the
representation of users and items from a user-item graph. In the state-of-the-art, there are …

Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …

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 …

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 is effective in improving the recommendation performance by
leveraging social relations from online social networking platforms. Social relations among …

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

Data in many real-world applications such as social networks, users shopping behaviors,
and inter-item relationships can be represented as graphs. Graph Neural Networks (GNNs) …

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

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 …

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 …