Heterogeneous graph neural networks (HGNNs) play an important role in accomplishing
node classification on heterogeneous graphs (HGs). These models are built on the …

Incremental recommendation systems have garnered significant research interest since they
ideally adapt to users' ongoing events (such as clicking, browsing, and reviewing) and …

In recent years, review-based methods have been widely used to learn user representations
because reviews contain abundant information. However, few users would like to write …

Collaborative filtering-based methods have achieved distinctive performance in ordinary
recommendation tasks. However, they suffer from a cold-start problem when historical …

The cold-start problem has been of great concern in the recommendation domain. To
address this problem, meta-learning frameworks have been widely adopted due to their fast …

Session-based recommendations (SBRs) make recommendations using the current
interaction sequence of users. Recent studies on SBRs have primarily used graph neural …

Learning on temporal heterogeneous graphs (THGs) has attracted substantial attention in
applications of information retrieval. Such graphs are ubiquitous in real-world domains like …

Graph convolution neural networks have shown powerful ability in recommendation, thanks
to extracting the user-item collaboration signal from users' historical interaction information …

Social recommendation leverages social network to complement user–item interaction data
for recommendation task, aiming to mitigate the data sparsity issue in recommender …

Accurate delay evolution prediction plays a pivotal role in train rescheduling decision-
making for the railway network. Existing studies on delay prediction predominantly centered …