Machine Learning Approaches to Hybrid Music Recommender Systems

  • Andreu VallEmail author
  • Gerhard Widmer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)


Music recommender systems have become a key technology supporting the access to increasingly larger music catalogs in on-line music streaming services, on-line music shops, and private collections. The interaction of users with large music catalogs is a complex phenomenon researched from different disciplines. We survey our works investigating the machine learning and data mining aspects of hybrid music recommender systems (i.e., systems that integrate different recommendation techniques). We proposed hybrid music recommender systems robust to the so-called “cold-start problem” for new music items, favoring the discovery of relevant but non-popular music. We thoroughly studied the specific task of music playlist continuation, by analyzing fundamental playlist characteristics, song feature representations, and the relationship between playlists and the songs therein.


Music recommender systems Music playlist continuation Hybrid recommender systems Cold-start problem 



This research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 670035 (Con Espressione).


  1. 1.
    Aizenberg, N., Koren, Y., Somekh, O.: Build your own music recommender by modeling internet radio streams. In: Proceedings of WWW, pp. 1–10 (2012)Google Scholar
  2. 2.
    Celma, Ò.: Music Recommendation and Discovery. Springer, Heidelberg (2010). Scholar
  3. 3.
    Chen, S., Moore, J.L., Turnbull, D., Joachims, T.: Playlist prediction via metric embedding. In: Proceedings of SIGKDD, pp. 714–722 (2012)Google Scholar
  4. 4.
    DiCiccio, T.J., Efron, B.: Bootstrap confidence intervals. Stat. Sci. 11, 189–212 (1996)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Flexer, A., Schnitzer, D., Gasser, M., Widmer, G.: Playlist generation using start and end songs. In: Proceedings of ISMIR, pp. 173–178 (2008)Google Scholar
  6. 6.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of ICDM, pp. 263–272 (2008)Google Scholar
  7. 7.
    Knees, P., Pohle, T., Schedl, M., Widmer, G.: Combining audio-based similarity with web-based data to accelerate automatic music playlist generation. In: Proceedings of the International Workshop on Multimedia Information Retrieval, pp. 147–154 (2006)Google Scholar
  8. 8.
    Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook, 2nd edn. Springer, Boston (2015). Scholar
  9. 9.
    Vall, A.: Listener-inspired automated music playlist generation. In: Proceedings of RecSys, Vienna, Austria (2015)Google Scholar
  10. 10.
    Vall, A., Dorfer, M., Schedl, M., Widmer, G.: A hybrid approach to music playlist continuation based on playlist-song membership. In: Proceedings of SAC, Pau, France (2018)Google Scholar
  11. 11.
    Vall, A., Eghbal-zadeh, H., Dorfer, M., Schedl, M.: Timbral and semantic features for music playlists. In: Machine Learning for Music Discovery Workshop at ICML, New York, NY, USA (2016)Google Scholar
  12. 12.
    Vall, A., Eghbal-zadeh, H., Dorfer, M., Schedl, M., Widmer, G.: Music playlist continuation by learning from hand-curated examples and song features: Alleviating the cold-start problem for rare and out-of-set songs. In: Proceedings of the Workshop on Deep Learning for Recommender Systems at RecSys, Como, Italy (2017)Google Scholar
  13. 13.
    Vall, A., Quadrana, M., Schedl, M., Widmer, G.: The importance of song context and song order in automated music playlist generation. In: Proceedings of ICMPC-ESCOM, Graz, Austria (2018)Google Scholar
  14. 14.
    Vall, A., Schedl, M., Widmer, G., Quadrana, M., Cremonesi, P.: The importance of song context in music playlists. In: RecSys Poster Proceedings, Como, Italy (2017)Google Scholar
  15. 15.
    Vall, A., Skowron, M., Knees, P., Schedl, M.: Improving music recommendations with a weighted factorization of the tagging activity. In: Proceedings of ISMIR, Málaga, Spain (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Computational PerceptionJohannes Kepler UniversityLinzAustria
  2. 2.Austrian Research Institute for Artificial IntelligenceViennaAustria

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