In the recommendation system, collaborative filtering methods based on the graph
convolution network can explicitly model the interaction between the nodes of the user–item …

Graph neural networks have revealed powerful potential in ranking recommendation.
Existing methods based on bipartite graphs for ranking recommendation mainly focus on …

Recently, graph neural networks (GNNs) have been widely used to develop successful
recommender systems. Although powerful, it is very difficult for a GNN-based recommender …

There has been sustained interest from both academia and industry throughout the years
due to the importance and practicability of recommendation systems. However, several …

Contrastive-learning-based neural networks have recently been introduced to recommender
systems, due to their unique advantage of injecting collaborative signals to model deep …

In recent years, graph neural networks (GNNs) have made great progress in
recommendation. The core mechanism of GNNs-based recommender system is to iteratively …

Recently, one critical issue looms large in the field of recommender systems–there are no
effective benchmarks for rigorous evaluation–which consequently leads to unreproducible …

The collaborative filtering (CF) problem with only user-item interaction information can be
solved by graph signal processing (GSP), which uses low-pass filters to smooth the …

The recommendation algorithm is an important means to alleviate the information explosion
in the era of big data. There has been a great deal of research into the use of knowledge …

The sequential recommendation (also known as the next-item recommendation), which aims
to predict the following item to recommend in a session according to users' historical …