Graph convolution networks (GCNs) play an increasingly vital role in recommender systems,
due to their remarkable relation modeling and representation capabilities. Concretely, they …

Most existing collaborative filtering-based recommender systems rely solely on available
user–item interactions for user and item representation learning. Their performance often …

Graph convolutional networks (GCNs) have been widely used to learn high-quality
representations (aka embeddings) from multiorder neighbors in recommendation tasks …

Graph convolutional network (GCN) has been extensively applied to recommender systems
(RS) and achieved significant performance improvements through iteratively aggregating …

Graph convolutional networks (GCNs) have shown great potential in recommender systems.
GCN models contain multiple layers of graph convolutions to exploit signals from higher …

Although current graph convolutional network (GCN) has achieved competitive performance
in personalized recommendation systems, most of existing GCN based recommendation …

Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for
Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to …

Graph convolutional networks are widely used in recommendation tasks owing to their
ability to learn user and item embeddings using collaborative signals from high-order …

Graph convolution neural networks have shown powerful ability in recommendation, thanks
to extracting the user-item collaboration signal from users' historical interaction information …

Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is
attributed to their capability on learning good user and item embeddings by exploiting the …