Graph neural networks (GNNs) have achieved remarkable success in recommender
systems by representing users and items based on their historical interactions. However …

Graph Neural Network (GNN) based recommender systems have been attracting more and
more attention in recent years due to their excellent performance in accuracy. Representing …

With the explosive growth of online information, many recommendation methods have been
proposed. This research direction is boosted with deep learning architectures, especially the …

Recommender models aimed at mining users' behavioral patterns have raised great
attention as one of the essential applications in daily life. Recent work on graph neural …

With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Recently, Graph Neural Networks (GNNs) have become a mainstream recommender system
method, where it captures high-order collaborative signals between nodes by performing …

With the explosive growth of online information, many recommendation methods have been
proposed. This research direction is boosted with deep learning architectures, especially the …

Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Since user-item interactions in recommender systems can be naturally modeled as a
bipartite graph, recent studies have started to incorporate graph neural networks (GNNs) to …

Recommender systems mitigate information overload by offering personalized suggestions
to users. As the interactions between users and items can inherently be depicted as a …