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 music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group mus...

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Bibliographic Details
Published in:Information (Basel) 2021-12, Vol.12 (12), p.506
Main Authors: Valera, Adrián, Lozano Murciego, Álvaro, Moreno-García, María N.
Format: Article
Language:English
Subjects:
Algorithms
Collaboration
collaborative filtering
Comparative studies
Context
context-aware recommender systems
Domains
group recommender systems
Motion pictures
Music
music recommendation
Ratings & rankings
Recommender systems
User behavior
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Summary:Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.
ISSN:2078-2489
2078-2489
DOI:10.3390/info12120506