Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …

Recommender systems have been demonstrated to be effective to meet user's personalized
interests for many online services (eg, E-commerce and online advertising platforms) …

Recent years have witnessed the great accuracy performance of graph-based Collaborative
Filtering (CF) models for recommender systems. By taking the user-item interaction behavior …

Topic-based communities have gradually become a considerable medium for netizens to
disseminate and acquire knowledge. These communities consist of entities (actual objects …

Cross-media retrieval of scientific and technological information is one of the important tasks
in the cross-media study. Cross-media scientific and technological information retrieval …

Recommendation systems are of crucial importance due to their wide applications.
Knowledge graph (KG) enabled recommendation schemes have attracted great attention …

Data sparsity is a common issue for most recommender systems and can severely degrade
the usefulness of a system. One of the most successful solutions to this problem has been …

Knowledge tracing (KT) is the task of tracing students' evolving knowledge state during
learning, which has improved the learning efficiency. To facilitate KT's development, most …

In recent years, the recommender system has been widely used in online platforms, which
can extract useful information from giant volumes of data and recommend suitable items to …

Most graph convolutional network (GCN)-based social recommendation frameworks fuse
social links with user-item interactions to enrich user representations, which alleviate the …