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

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

Social recommendation has emerged to leverage social connections among users for
predicting users' unknown preferences, which could alleviate the data sparsity issue in …

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

Social recommendation which aims to leverage social connections among users to enhance
the recommendation performance. With the revival of deep learning techniques, many efforts …

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 …

With the prevalence of online social platforms, social recommendation has emerged as a
promising direction that leverages the social network among users to enhance …