With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

In the information explosion era, recommender systems (RSs) are widely studied and
applied to discover user-preferred information. A RS performs poorly when suffering from the …

Hypergraph Convolutional Network (HCN) has be-come a proper choice for capturing high-
order relationships. Existing HCN methods are tailored for static hypergraphs, which are …

The development of personalized recommendation has significantly improved the accuracy
of information matching and the revenue of e-commerce platforms. Recently, it has two …

In an era of information explosion, recommender systems are vital tools to deliver
personalized recommendations for users. The key of recommender systems is to forecast …

Personalized recipe recommender systems help users mine certain dishes they want to find
and even really desire, which play a significant role in matching dishes, balancing nutrients …

Biomedical Relation Extraction (RE) systems identify and classify relations between
biomedical entities to enhance our knowledge of biological and medical processes. Most …

In recent practices, sparsity problems often arise in recommendation systems, resulting in
weak generalization ability. To alleviate this problem, tag-aware recommendation systems …

Knowledge graph (KG), which contains rich side information, becomes an essential part to
boost the recommendation performance and improve its explainability. However, existing …

Recommender systems are popularly used to deal with an information overload issue.
Existing systems mainly focus on user–item interactions and semantic information derived …