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 …

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 network (GNN) has become the new state-of-the-art approach in
many recommendation problems, with its strong ability to handle structured data and to …

Graph neural networks (GNNs) have recently emerged as state-of-the-art collaborative
filtering (CF) solution. A fundamental challenge of CF is to distill negative signals from the …

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

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 …

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

Graph-based recommender system has attracted widespread attention and produced a
series of research results. Because of the powerful high-order connection modeling …

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 …

Personalized recommendation plays an important role in many online services. Substantial
research has been dedicated to learning embeddings of users and items to predict a user's …