Graph Neural Network (GNN) based social recommendation models improve the prediction
accuracy of user preference by leveraging GNN in exploiting preference similarity contained …

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

Social recommendation aims to fuse social links with user-item interactions to alleviate the
cold-start problem for rating prediction. Recent developments of Graph Neural Networks …

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 …

Incorporating social relations into the recommendation system, ie social recommendation,
has been widely studied in academic and industrial communities. While many promising …

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 …

The recommender system is of great significance to alleviate information overload. The rise
of online social networks leads to a promising direction—social recommendation. By …

In recent years, researchers attempt to utilize online social information to alleviate data
sparsity for collaborative filtering, based on the rationale that social networks offers the …

In recent years, advances in Graph Convolutional Networks (GCNs) have given new insights
into the development of social recommendation. However, many existing GCN-based social …

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) …