Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative
filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well …

To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …

Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-
Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich …

Data sparsity is a challenge problem that most modern recommender systems are
confronted with. By leveraging the knowledge from relevant domains, the cross-domain …

Recommender systems have been widely deployed in many real-world applications, but
usually suffer from the long-standing user cold-start problem. As a promising way, Cross …

In a large recommender system, the products (or items) could be in many different
categories or domains. Given two relevant domains (eg, Book and Movie), users may have …

Shared-account Cross-domain Sequential recommendation (SCSR) is the task of
recommending the next item based on a sequence of recorded user behaviors, where …

Cross-domain recommendation (CDR) aims to provide better recommendation results in the
target domain with the help of the source domain, which is widely used and explored in real …