With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

Music recommender systems (MRSs) have experienced a boom in recent years, thanks to
the emergence and success of online streaming services, which nowadays make available …

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

In the past decade, recommender systems have been widely used to provide users with
personalized products and services. However, most traditional recommender systems are …

Cross-domain recommendation has long been one of the major topics in recommender
systems. Recently, various deep models have been proposed to transfer the learned …

Electronic commerce or e-commerce includes the service and good exchange through
electronic support like the Internet. It plays a crucial role in today's business and users' …

Cross-domain recommendation aims to leverage knowledge from multiple domains to
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …