Recent years have witnessed the fast development of the emerging topic of Graph Learning
based Recommender Systems (GLRS). GLRS employ advanced graph learning …

Recent years have witnessed the fast development of the emerging topic of Graph Learning
based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning …

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 …

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 …

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 …

Inspired by their powerful representation ability on graph-structured data, Graph Convolution
Networks (GCNs) have been widely applied to recommender systems, and have shown …

Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative
filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN …

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

Graph neural network (GNN) is a powerful learning approach for graph-based recommender
systems. Recently, GNNs integrated with contrastive learning have shown superior …

Graph convolution networks (GCNs) for recommendations have emerged as an important
research topic due to their ability to exploit higher-order neighbors. Despite their success …