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

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 …

Business Ecosystem (BE) has been defined in multiple ways and has been used
interchangeably, jointly and overlap** with innovation ecosystems (IE) and digital …

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

Collaborative filtering (CF) is a major technique in recommender systems to help users find
their potentially desired items. Since the data sparsity problem is quite commonly …

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 …

Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …

Web systems that provide the same functionality usually share a certain amount of items.
This makes it possible to combine data from different websites to improve recommendation …

Cross-domain recommendation is an emerging research topic. In the last few years an
increasing amount of work has been published in various areas related to the …

Recommender systems always aim to provide recommendations for a user based on
historical ratings collected from a single domain (eg, movies or books) only, which may …