Social recommendation aims at augmenting user-item interaction relationships and boosting
recommendation quality by leveraging social information. Recently, self-supervised learning …

Purpose This paper addresses the limitations of current graph neural network-based
recommendation systems, which often neglect the integration of side information and the …

Graphs are a foundational way to represent scenarios where objects interact in pairs.
Recently, graph neural networks (GNNs) have become widely used for modeling simple …

Abstract Knowledge Tracing (KT) is a method that seeks to forecast students' future
performance based on their historical interactions with intelligent tutoring systems. Various …

Representation learning in unlabeled heterogeneous graphs has gained significant interest.
The heterogeneity in graphs not only provides rich information but also poses challenges to …

Heterogeneous graph representation learning gains popularity due to its powerful
capabilities of feature extraction and numerous related algorithms have emerged for various …

E-commerce platforms should provide detailed product descriptions (attribute values) for
effective product search and recommendation. However, attribute value information is …

Graph neural network (GNN) models have garnered considerable interest in the
development of recommender systems, owing to their capability to represent users and/or …

In recent years, hypergraph models have gained widespread attention in the hypergraph
node classification task due to their ability to capture high-order node relationships …

Abstract Information extraction aims to automatically extract structured information from
unstructured texts. Supervised information extraction requires large quantities of labeled …