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Recommender Systems Handbook pp 453–492Cite as

Music Recommender Systems

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Abstract

This chapter gives an introduction to music recommender systems research. We highlight the distinctive characteristics of music, as compared to other kinds of media. We then provide a literature survey of content-based music recommendation, contextual music recommendation, hybrid methods, and sequential music recommendation, followed by overview of evaluation strategies and commonly used data sets. We conclude by pointing to the most important challenges faced by music recommendation research.

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Notes

  1. 1.

    We will not further detail collaborative filtering of music ratings in this chapter. To understand the principles of this technique, we refer the reader to Chap. 2

  2. 2.

    To avoid confusion, we note that content has different connotations within the MIR and recommender systems communities. MIR makes an explicit distinction between (content-based) approaches that operate directly on audio signals and (metadata) approaches that derive item descriptors from external sources, e.g., web documents [70]. In recommender systems research, as in the remainder of this chapter, both types of approaches are described as “content-based”.

  3. 3.

    http://www.musicbrainz.org.

  4. 4.

    http://www.discogs.com.

  5. 5.

    http://www.pandora.com.

  6. 6.

    http://www.allmusic.com.

  7. 7.

    http://www.last.fm.

  8. 8.

    http://www.wikipedia.org.

  9. 9.

    http://www.amazon.com.

  10. 10.

    http://www.bbc.co.uk.

  11. 11.

    http://www.billboard.com.

  12. 12.

    http://www.pitchforkmedia.com.

  13. 13.

    http://bluebrainmusic.blogspot.com/.

  14. 14.

    http://www.dbpedia.org.

  15. 15.

    http://www.musicovery.com.

  16. 16.

    http://www.artofthemix.org.

  17. 17.

    http://zune.net; now Xbox Music.

  18. 18.

    http://www.shoutcast.com.

  19. 19.

    In AotM-2011, this figure refers to the sum of the length of all playlists, where length is measured as the number of songs.

  20. 20.

    For AotM-2011 this is partially the case, as not all playlist categories refer to contextual factors.

  21. 21.

    http://the.echonest.com.

  22. 22.

    http://www.7digital.com.

  23. 23.

    http://developer.7digital.com/resources/api-docs.

  24. 24.

    There exist many more music benchmarking activities which are oriented towards retrieval or annotation, e.g., MIREX (http://www.music-ir.org/mirex/wiki) or MusiClef (http://www.cp.jku.at/datasets/musiclef).

  25. 25.

    http://www.sigkdd.org/kdd2011/kddcup.shtml.

  26. 26.

    http://music.yahoo.com.

  27. 27.

    http://labrosa.ee.columbia.edu/millionsong.

  28. 28.

    http://www.musixmatch.com.

  29. 29.

    http://www.secondhandsongs.com.

  30. 30.

    http://labrosa.ee.columbia.edu/millionsong/challenge.

  31. 31.

    http://www.kaggle.com/c/msdchallenge.

  32. 32.

    http://ocelma.net/MusicRecommendationDataset.

  33. 33.

    http://www.cp.jku.at/datasets/musicmicro and http://www.cp.jku.at/datasets/MMTD, resp.

  34. 34.

    http://bmcfee.github.io/data/aotm2011.html.

  35. 35.

    http://www.artofthemix.org.

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Authors and Affiliations

  1. Department of Computational Perception, Johannes Kepler University Linz, Linz, Austria

    Markus Schedl & Peter Knees

  2. Center for Data Science, New York University, New York, NY, USA

    Brian McFee

  3. Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain

    Dmitry Bogdanov

  4. Insight Centre for Data Analytics, University College Cork, Cork, Ireland

    Marius Kaminskas

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  1. Markus Schedl
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  2. Peter Knees
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  3. Brian McFee
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  4. Dmitry Bogdanov
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  5. Marius Kaminskas
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Correspondence to Markus Schedl .

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  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Schedl, M., Knees, P., McFee, B., Bogdanov, D., Kaminskas, M. (2015). Music Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_13

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