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 …

Recommendation systems have become popular and effective tools to help users discover
their interesting items by modeling the user preference and item property based on implicit …

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 …

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning
tasks, showing superior generalization ability. Inspired by the success, adopting CL into …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

Recently, graph neural networks (GNN) have been successfully applied to recommender
systems as an effective collaborative filtering (CF) approach. However, existing GNN-based …

With the tremendous success of Graph Convolutional Networks (GCNs), they have been
widely applied to recommender systems and have shown promising performance. However …

Collaborative Filtering (CF) models, despite their great success, suffer from severe
performance drops due to popularity distribution shifts, where these changes are ubiquitous …

Applying a graph convolutional network (GCN) or its variants to user-item interaction graphs
is one of the most commonly used approaches for learning the representation of users and …

Considering the prevalence of the power-law distribution in user-item networks, hyperbolic
space has attracted considerable attention and achieved impressive performance in the …