Online communities such as Facebook and Twitter are enormously popular and have
become an essential part of the daily life of many of their users. Through these platforms …

In many applications of session-based recommendation, social networks are usually
available. Since users' interests are influenced by their friends, recommender systems can …

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 …

Group recommendation has become highly demanded when users communicate in the
forms of group activities in online sharing communities. These group activities include …

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

Recommendation system utilizes user-item interactions and information on user's attributes
to infer the user's interests and use them to make recommendations for the user. Graph …

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 …

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

Collaborative filtering (CF) is one of the most popular techniques for building recommender
systems. To alleviate the data sparsity issue in CF, social recommendation has emerged by …