Over the past two decades, a large amount of research effort has been devoted to
develo** algorithms that generate recommendations. The resulting research progress has …

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

Recommendation technology is an important part of the Internet of Things (IoT) services,
which can provide better service for users and help users get information anytime …

The cross-domain recommendation technique is an effective way of alleviating the data
sparse issue in recommender systems by leveraging the knowledge from relevant domains …

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 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 …

User-based collaborative filtering, a widely used nearest neighbour-based recommendation
technique, predicts an item's rating by aggregating its ratings from similar users. User …

Recently, more and more mobile apps are employed in the marketing field with technical
advances. Mobile marketing apps have become a prevalent way for enterprise marketing …

The proliferation of e-commerce sites and online social media has allowed users to provide
preference feedback and maintain profiles in multiple systems, reflecting a variety of their …

Cross domain recommender systems (CDRS) can assist recommendations in a target
domain based on knowledge learned from a source domain. CDRS consists of three …