Chapter 13 Music Recommender Systems Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov, and Marius Kaminskas 13.1 Introduction Boosted by the emergence of online music shops and music streaming services, digital music distribution has led to an ubiquitous availability of music. Music listeners, suddenly faced with an unprecedented scale of readily available content, can easily become overwhelmed. Music recommender systems, the topic of this chapter, provide guidance to users navigating large collections. Music items that can be recommended include artists, albums, songs, genres, and radio stations. In this chapter, we illustrate the unique characteristics of the music recommen- dation problem, as compared to other content domains, such as books or movies. To understand the differences, let us first consider the amount of time required for ausertoconsumeasinglemediaitem.Thereisobviouslyalargediscrepancyin consumption time between books (days or weeks), movies (one to a few hours), and asong(typicallyafewminutes).Consequently,thetimeittakesforausertoform opinions for music can be much shorter than in other domains, which contributes to the ephemeral, even disposable, nature of music. Similarly, in music, a single M. Schedl (!)•P.Knees Department of Computational Perception, Johannes Kepler University Linz, Linz, Austria e-mail:
[email protected];
[email protected] B. McFee Center for Data Science, New York University, New York, NY, USA e-mail:
[email protected] D. Bogdanov Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain e-mail:
[email protected] M. Kaminskas Insight Centre for Data Analytics, University College Cork, Cork, Ireland e-mail:
[email protected] ©SpringerScience+BusinessMediaNewYork2015 453 F. Ricci et al. (eds.), Recommender Systems Handbook, DOI 10.1007/978-1-4899-7637-6_13 454 M.