Context-Aware Music Recommender Systems for Groups: A Comparative Study

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the domain, these especially useful, since users often like to listen new songs and discover bands. At same time, group consumption has proliferated this not just physically, past, but virtually rooms or messaging groups created for specific purposes, studying, training, meeting friends. Single-user no longer valid situation, needed recommend of users, taking into account their individual preferences context (when listening music). paper, a system domain is proposed, an extensive comparative study conducted, involving different collaborative filtering algorithms aggregation methods.