Recommendation systems are vital tools for helping users discover content that suits their
interests. Collaborative filtering methods are one of the techniques employed for analyzing …

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-
art accuracy on recommender system (RecSys) benchmarks. However, recommendation …

Graph Neural Networks (GNNs) have been successfully adopted in recommender systems
by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless …

While Graph Convolutional Networks (GCNs) have shown great potential in recommender
systems and collaborative filtering (CF), they suffer from expensive computational complexity …

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

In recent years, owing to the outstanding performance in graph representation learning,
graph neural network (GNN) techniques have gained considerable interests in many real …

An essential weakness of existing personalized recommender systems is that the learning is
biased and dominated by popular items and users. Existing methods, particularly graph …

Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative
filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing …

Recommendation systems have advanced significantly in recent years, achieving greater
accuracy and relevance. However, traditional approaches often suffer from a mismatch …

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