Human Intelligence is considered superior compared to Artificial Intelligence (AI) because of
its ability to adapt faster to changes. Due to increasing data deluge, it is cumbersome for …

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 collaborative filtering recommender systems user's preferences are expressed as ratings
for items, and each additional rating extends the knowledge of the system and affects the …

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 …

The new user problem in recommender systems is still challenging, and there is not yet a
unique solution that can be applied in any domain or situation. In this paper we analyze …

Due to the extensive growth of food varieties, making better and healthier food choices
becomes more and more complex. Most of the current food suggestion applications offer just …

Providing relevant personalized recommendations for new users is one of the major
challenges in recommender systems. This problem, known as the user cold start has been …

Besides the simple human intelligence tasks such as image labeling, crowdsourcing
platforms propose more and more tasks that require very specific skills, especially in …

As recommender systems become pervasive in various scenarios, cross-domain
recommenders (CDR) are proposed to enhance the performance of one target domain with …