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Music Recommendation Systems: Techniques, Use Cases, and Challenges

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Recommender Systems Handbook

Abstract

This chapter gives an introduction to music recommender systems, considering the unique characteristics of the music domain. We take a user-centric perspective, by organizing our discussion with respect to current use cases and challenges. More precisely, we categorize music recommendation tasks into three major types of use cases: basic music recommendation, lean-in exploration, and lean-back listening. Subsequently, we explain the main categories of music recommender systems from a technical perspective, including content-based filtering, sequential recommendation, and recent psychology-inspired approaches. To round off the chapter, we provide a discussion of challenges faced in music recommendation research and practice, and of approaches that address these challenges. Topics we address here include creating multi-faceted recommendation lists, considering intrinsic user characteristics, making fair recommendations, explaining recommendations, evaluation, dealing with missing and negative feedback, designing user interfaces, and providing open tools and data sources.

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Notes

  1. 1.

    https://www.spotify.com.

  2. 2.

    https://www.pandora.com.

  3. 3.

    https://www.apple.com/apple-music.

  4. 4.

    https://music.amazon.com.

  5. 5.

    https://www.youtube.com.

  6. 6.

    https://www.deezer.com.

  7. 7.

    https://www.kkbox.com.

  8. 8.

    https://www.melon.com.

  9. 9.

    https://www.boomplay.com.

  10. 10.

    https://www.superplayer.fm.

  11. 11.

    https://www.oktav.com.

  12. 12.

    https://www.chordify.net.

  13. 13.

    https://www.netflixprize.com.

  14. 14.

    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 [90]. In recommender systems research, as in the remainder of this chapter, both types of approaches are described as “content-based”.

  15. 15.

    https://www.soundcloud.com.

  16. 16.

    https://www.last.fm.

  17. 17.

    https://www.apple.com/itunes.

  18. 18.

    https://www.amazon.com.

  19. 19.

    https://www.discogs.com.

  20. 20.

    https://www.rateyourmusic.com.

  21. 21.

    Note that explicit ratings can be estimated from implicit feedback such as play counts, as investigated by Parra and Amatriain [137].

  22. 22.

    https://www.kdd.org/kdd2011/kddcup.shtml.

  23. 23.

    http://music.yahoo.com.

  24. 24.

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

  25. 25.

    https://www.recsyschallenge.com/2018.

  26. 26.

    The Million Playlist Dataset is available from https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge.

  27. 27.

    https://www.crowdai.org/challenges/spotify-sequential-skip-prediction-challenge.

  28. 28.

    https://www.oktav.com.

  29. 29.

    https://www.chordify.net.

  30. 30.

    Note that perspective (2) most commonly also entails (1) since different recommendation techniques require different data to operate on.

  31. 31.

    https://wiki.dbpedia.org.

  32. 32.

    Note that we use the term “interaction data” in Sect. 4 to refer to data belonging to the latter kind of context.

  33. 33.

    https://www.music-ir.org/mirex/wiki.

  34. 34.

    This scenario is addressed in MRS that leverage cognitive models of frequency and recentness of exposure, discussed in Sect. 3.6.

  35. 35.

    Note that these ratings can also be binary (1 if the user interacted with the item; 0 otherwise).

  36. 36.

    The Melon Playlist Dataset is a notable exception, containing data from a South Korean music streaming service.

  37. 37.

    Notwithstanding, there also exist offline variants of A/B testing strategies, e.g. [63].

  38. 38.

    https://www.youtube.com.

  39. 39.

    https://www.douban.com.

  40. 40.

    https://making.lyst.com/lightfm/docs.

  41. 41.

    https://implicit.readthedocs.io.

  42. 42.

    http://surpriselib.com.

  43. 43.

    https://guoguibing.github.io/librec.

  44. 44.

    https://github.com/spotify/annoy.

  45. 45.

    https://github.com/nmslib/nmslib.

  46. 46.

    https://github.com/facebookresearch/faiss.

  47. 47.

    https://essentia.upf.edu.

  48. 48.

    https://librosa.org.

  49. 49.

    https://www.musicbrainz.org.

  50. 50.

    https://www.listenbrainz.org.

  51. 51.

    https://www.acousticbrainz.org.

  52. 52.

    https://www.discogs.com.

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Acknowledgements

We would like to thank Marius Kaminskas for contributing to the previous version of this chapter, in the second edition of this book.

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Authors and Affiliations

  1. Johannes Kepler University Linz, Institute of Computational Perception, Multimedia Mining and Search Group, Linz, Austria

    Markus Schedl

  2. LIT AI Lab, Human centered AI Group, Linz, Austria

    Markus Schedl

  3. TU Wien, Faculty of Informatics, Institute of Information Systems Engineering, Vienna, Austria

    Peter Knees

  4. New York University, Center for Data Science and Music and Audio Research Lab, New York, NY, USA

    Brian McFee

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

    Dmitry Bogdanov

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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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Correspondence to Markus Schedl .

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Editors and Affiliations

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

    Francesco Ricci

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

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Schedl, M., Knees, P., McFee, B., Bogdanov, D. (2022). Music Recommendation Systems: Techniques, Use Cases, and Challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_24

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