Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

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 …

In recent years, recommender system has become an indispensable function in all e-
commerce platforms. The review rating data for a recommender system typically comes from …

Recently, user cold-start recommendations have attracted a lot of attention from industry and
academia. In user cold-start recommendation systems, the user attribute information is often …

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

Cold-start problem is a fundamental challenge for recommendation tasks. Despite the recent
advances on Graph Neural Networks (GNNs) incorporate the high-order collaborative signal …

The recent methods for cross-network node classification mainly exploit graph neural
networks (GNNs) as feature extractor to learn expressive graph representations across the …

Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG)
for recommendation. Existing GNN-based methods explicitly model the dependency …

Recently, graph neural networks have been widely used for network embedding because of
their prominent performance in pairwise relationship learning. In the real world, a more …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …