Recommender systems have become prosperous nowadays, designed to predict users'
potential interests in items by learning embeddings. Recent developments of the Graph …

With the widespread popularization of social network platforms, user-generated content and
other social network data are growing rapidly. It is difficult for social users to select interested …

Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in
learning distributed representations from graph-structured data. However, centralizing a …

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 …

Detecting hot social events (eg, political scandal, momentous meetings, natural hazards,
etc.) from social messages is crucial as it highlights significant happenings to help people …

Sequential Recommendation characterizes the evolving patterns by modeling item
sequences chronologically. The essential target of it is to capture the item transition …

Graph Neural Network (GNN) based social recommendation models improve the prediction
accuracy of user preference by leveraging GNN in exploiting preference similarity contained …

In the information explosion era, recommender systems (RSs) are widely studied and
applied to discover user-preferred information. A RS performs poorly when suffering from the …

Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item
interactions are entirely unavailable. The well-established, dominating identity (ID)-based …

Because of the large number of online games available nowadays, online game
recommender systems are necessary for users and online game platforms. The former can …