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

Collaborative filtering is one of the most fundamental topics for recommender systems.
Various methods have been proposed for collaborative filtering, ranging from matrix …

Incremental recommendation systems have garnered significant research interest since they
ideally adapt to users' ongoing events (such as clicking, browsing, and reviewing) and …

With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …

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 …

Sequential recommendation demonstrates the capability to recommend items by modeling
the sequential behavior of users. Traditional methods typically treat users as sequences of …

Dynamic interaction graphs have been widely adopted to model the evolution of user-item
interactions over time. There are two crucial factors when modelling user preferences for link …

The success of graph neural network-based models (GNNs) has significantly advanced
recommender systems by effectively modeling users and items as a bipartite, undirected …

Online recommenders have attained growing interest and created great revenue for
businesses. Given numerous users and items, incremental update becomes a mainstream …

The massive amount of data on the Internet of Things (IoT) drives recommendation systems
(RSs) based on graph neural network (GNN) to fully play a role in improving user …