Abstract Knowledge Graph Embedding (KGE)-enhanced recommender systems are
effective in providing accurate and personalized recommendations in diverse application …

Abstract Traditional Chinese Medicine (TCM) has been widely adopted in clinical practice by
Eastern Asia people for thousands of years. Nowadays, TCM still plays a critical role in …

We explore the semantic-rich structured information derived from the knowledge graphs
(KGs) associated with the user-item interactions and aim to reason out the motivations …

Graph neural networks (GNNs) have shown great success in various applications. As real-
world graphs are large, training GNNs in distributed systems is desirable. In current training …

Link prediction based on bipartite networks can not only mine hidden relationships between
different types of nodes, but also reveal the inherent law of network evolution. Existing …

In view of the perishable nature and complex storage requirements of certain blood
products, the delivery of blood groups from blood banks to hospitals is a key aspect of the …

Traditional POI recommendation results tend to be homogeneous due to data sparsity, and
cannot adapt to the dynamic switching of user locations. Regarding the issues, we propose …

Abstract Knowledge Graphs (KGs) which can encode structural relations connecting two
objects with one or multiple related attributes have become an increasingly popular …

Abstract Numerical Table-Text Question Answering aims to predict the program over
heterogeneous tabular and textual information, which has recently attracted strong attention …

Existing research on recommender systems primarily focuses on improving a single
objective, such as prediction accuracy, often ignoring other crucial aspects of …