Abstract
Music recommendation systems have become a valuable aid for managing large music collections and discovering new music. Our content-based recommendation system employs signal-based features and semantic music attributes generated using machine-based learning algorithms. In addition to playlist generation and music recommendation, we are exploring new usability concepts made possible by the analysis results. Functionality such as the mufin vision sound universe enables the user to discover his own music collection or even unknown catalogues in a new, more intuitive way.
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References
Bahanovich, D., Collopy, D.: Music Experience and Behaviour in Young People. University of Hertfordshire, UK (2009)
Celma, O.: Music Recommendation and Discovery in the Long Tail. PhD-Thesis, Universitat Pompeu Fabra, Spain (2008)
Nielsen Soundscan: State of the industrie (2007), http://www.narm.com/2008Conv/StateoftheIndustry.pdf (July 22, 2009)
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© 2011 Springer-Verlag Berlin Heidelberg
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Schönfuß, D. (2011). Content-Based Music Discovery. In: Ystad, S., Aramaki, M., Kronland-Martinet, R., Jensen, K. (eds) Exploring Music Contents. CMMR 2010. Lecture Notes in Computer Science, vol 6684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23126-1_22
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DOI: https://doi.org/10.1007/978-3-642-23126-1_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23125-4
Online ISBN: 978-3-642-23126-1
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