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 …

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering
(CF) approaches for recommender systems. The key idea of GNN-based recommender …

Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet
challenging task that simultaneously considers the shared-account and cross-domain …

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …

Neural graph collaborative filtering has received great recent attention due to its power of
encoding the high-order neighborhood via the backbone graph neural networks. However …

Graph neural network (GNN) based algorithms have achieved superior performance in
recommendation tasks due to their advanced capability of exploiting high-order connectivity …

Session-based recommendation aims to predict items that a user will interact with based on
historical behaviors in anonymous sessions. It has long faced two challenges:(1) the …

Sequential recommendation (SR) learns from the temporal dynamics of user-item
interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of …

With the ever-increasing popularity of microservice architecture, a considerable number of
enterprises or organizations have encapsulated their complex business services into …