With the rapid proliferation of scientific literature, versatile academic knowledge services
increasingly rely on comprehensive academic graph mining. Despite the availability of …

Modeling the dynamics into graph neural networks (GNNs) contributes to the understanding
of evolution in dynamic graphs, which helps optimize temporal-spatial representations for …

Increasing multiple behavior recommendation models have achieved great successes.
However, many models do not consider commonalities and differences between behaviors …

Collaborative filtering (CF) is a widely employed technique that predicts user preferences
based on past interactions. Negative sampling plays a vital role in training CF-based models …

Graph contrastive learning has emerged as a powerful technique for dealing with graph
noise and mining latent information in networks, that has been widely applied in GNN-based …

Acquiring reviewers for academic submissions is a challenging recommendation scenario.
Recent graph learning-driven models have made remarkable progress in the field of …

Self-supervised learning (SSL) has recently achieved great success in mining the user-item
interactions for collaborative filtering. As a major paradigm, contrastive learning (CL) based …

Digital publishing's exponential growth has created vast scholarly collections. Guiding
researchers to relevant resources is crucial, and knowledge graphs (KGs) are key tools for …

Data sparsity and the cold start problem significantly impede the advancement of
recommendation systems. Cross-domain recommendation (CDR) seeks to alleviate these …

Graph Neural Networks (GNNs) are commonly used and have shown promising
performance in recommendation systems. A major branch, Heterogeneous GNNs, models …