Cross-domain recommendation (CDR) aims at leveraging the correlation of users' behaviors
in both the source and target domains to improve the user preference modeling in the target …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

Sequential recommendation (SR) aims to model users' dynamic preferences from a series of
interactions. A pivotal challenge in user modeling for SR lies in the inherent variability of …

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised
node classification in recent years. However, the problem of insufficient supervision …

Machine learning has demonstrated remarkable performance over finite datasets, yet
whether the scores over the fixed benchmarks can sufficiently indicate the model's …

Graph neural networks (GNNs) have seen widespread usage across multiple real-world
applications, yet in transductive learning, they still face challenges in accuracy, efficiency …

With the rise of multimedia content on the internet, Multimodal Summarization has become a
challenging task to help individuals grasp vital information fast. However, previous methods …

We investigate node representation learning on text-attributed graphs (TAGs), where nodes
are associated with text information. Although recent studies on graph neural networks …

Graph Neural Networks (GNNs) have received considerable attention on graph-structured
data learning for a wide variety of tasks. The well-designed propagation mechanism, which …

Graph contrastive learning (GCL) has attracted rising research attention recently due to its
effectiveness in self-supervised graph learning. A key step of GCL is to conduct data …