Most popular recommender systems learn the embedding of users and items through
capturing valuable information from user–item interactions or item knowledge graph (KG) …

Heterogeneous graph neural networks (HGNNs) based on metapath exhibit powerful
capturing of rich structural and semantic information in the heterogeneous graph. HGNNs …

As auxiliary collaborative signals, the entity connectivity and relation semanticity beneath
knowledge graph (KG) triples can alleviate the data sparsity and cold-start issues of …

Solving cold-start problems is indispensable to provide meaningful recommendation results
for new users and items. Under sparsely observed data, unobserved user-item pairs are …

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

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

The recommendation system is widely used in many aspects of digital economy to offer
personalized services, in which efficient capture of user–item relations is of critical …

Many applications use recommender systems to predict user preferences, improve user
experience, and increase the amount of sales. However, because of the cold-start problem …

Multi-domain recommendation (MDR) aims to improve the recommendation performance in
all target domains simultaneously by leveraging rich data from relevant domains. However …

Collaborative Filtering (CF) is the key technique for recommender systems. CF exploits user-
item behavior interactions (eg, clicks) only and hence suffers from the data sparsity issue …