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 …

The music domain is among the most important ones for adopting recommender systems
technology. In contrast to most other recommendation domains, which predominantly rely on …

Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers …

Multimedia content is dominating today's Web information. The nature of multimedia user-
item interactions is 1/0 binary implicit feedback (eg, photo likes, video views, song …

Currently, the Internet contains a large amount of information, which must then be filtered to
determine suitability for certain users. Recommender systems are a very suitable tool for this …

Although latent factor models (eg, matrix factorization) achieve good accuracy in rating
prediction, they suffer from several problems including cold-start, non-transparency, and …

Personalized rating prediction is an important research problem in recommender systems.
Although the latent factor model (eg, matrix factorization) achieves good accuracy in rating …

While collaborative filtering is the dominant technique in personalized recommendation, it
models user-item interactions only and cannot provide concrete reasons for a …

Current recommender systems consider the various aspects of items for making accurate
recommendations. Different users place different importance to these aspects which can be …

Human actions are often captured by multiple cameras (or sensors) to overcome the
significant variations in viewpoints, background clutter, object speed, and motion patterns in …