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 …

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

With the emergence of personality computing as a new research field related to artificial
intelligence and personality psychology, we have witnessed an unprecedented proliferation …

A variety of approaches have been recently proposed to automatically infer users'
personality from their user generated content in social media. Approaches differ in terms of …

Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging
problems in computer vision and recommender system, respectively. In general, they are …

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

Personalized recommender systems have become indispensable in today's online world.
Most of today's recommendation algorithms are data-driven and based on behavioral data …

The recommender systems aim to predict potential demands of users by analyzing their
preferences and provide personalized recommendation services. User preferences can be …

This book discusses different aspects of group recommender systems which are systems
that help to identify recommendations for groups instead of single users. In this context, the …