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

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

In this era of information explosion, recommender systems have become increasingly vital in
research and everyday applications. Collaborative filtering and matrix factorization are …

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

Contrastive Learning (CL)-based recommender systems have gained prominence in the
context of Heterogeneous Graph (HG) due to their capacity to enhance the consistency of …

Self-supervised learning (SSL) has recently attracted significant attention in the field of
recommender systems. Contrastive learning (CL) stands out as a major SSL paradigm due …

Contrastive learning has been effectively utilized to enhance the training of sequential
recommendation models by leveraging informative self-supervised signals. Most existing …

Abstract In recent years, Graph Convolutional Networks (GCNs) have primarily been applied
to implicit feedback recommendation, with limited exploration in explicit scenarios. Although …

Malicious social bots achieve their malicious purposes by spreading misinformation and
inciting social public opinion, seriously endangering social security, making their detection a …

Utilizing alignment and uniformity for recommendation has shown success in considering
similarities between users and items. Despite this effectiveness, we argue that they suffer …