Advertisement

Introduction

  • Òscar CelmaEmail author
Chapter

Abstract

In recent years typical music consumption behaviour has changed dramatically. Personal music collections have grown, aided by technological improvements in networks, storage, portability of devices and Internet services. The number and the availability of songs have de-emphasised their value; it is usually the case that users own many digital music files that they have only listened to once, or not at all. It seems reasonable to suppose that with efficient ways to create a personalised order of users’ collections, as well as ways to explore hidden “treasures” inside them, the value of their music collections would drastically increase.

Keywords

Recommender System Recommendation Algorithm Music Information Retrieval Digital Music Music Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    U. Shardanand, “Social information filtering for music recommendation,” Master’s thesis, Massachussets Institute of Technology, Cambridge, MA, September 1994.Google Scholar
  2. 2.
    B. Logan, “Content-based playlist generation: Exploratory experiments,” in Proceedings of 3rd International Conference on Music Information Retrieval, (Paris, France), 2002.Google Scholar
  3. 3.
    B. Logan, “Music recommendation from song sets,” in Proceedings of 5th International Conference on Music Information Retrieval, (Barcelona, Spain), 2004.Google Scholar
  4. 4.
    W. Chai and B. Vercoe, “Using user models in music information retrieval systems,” in Proceedings of 1st International Conference on Music Information Retrieval, (Plymouth, MA, USA), 2000.Google Scholar
  5. 5.
    A. Uitdenbogerd and R. van Schnydel, “A review of factors affecting music recommender success,” in Proceedings of 3rd International Conference on Music Information Retrieval, (Paris, France), 2002.Google Scholar
  6. 6.
    O. Celma, M. Ramirez, and P. Herrera, “Foafing the music: A music recommendation system based on RSS feeds and user preferences,” in Proceedings of 6th International Conference on Music Information Retrieval, (London, UK), 2005.Google Scholar
  7. 7.
    R. van Gulik and F. Vignoli, “Visual playlist generation on the artist map,” in Proceedings of 6th International Conference on Music Information Retrieval, (London, UK), pp. 520–523, 2005.Google Scholar
  8. 8.
    E. Pampalk and M. Goto, “Musicsun: A new approach to artist recommendation,” in Proceedings of 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007.Google Scholar
  9. 9.
    S. Pauws and B. Eggen, “Pats: Realization and user evaluation of an automatic playlist generator,” in Proceedings of 3rd International Conference on Music Information Retrieval, (Paris, France), 2002.Google Scholar
  10. 10.
    E. Pampalk, T. Pohle, and G. Widmer, “Dynamic playlist generation based on skipping behavior,” in Proceedings of 6th International Conference on Music Information Retrieval, (London, UK), 2005.Google Scholar
  11. 11.
    E. Pampalk and M. Gasser, “An implementation of a simple playlist generator based on audio similarity measures and user feedback.,” in Proceedings of 7th International Conference on Music Information Retrieval, (Victoria, Canada), pp. 389–390, 2006.Google Scholar
  12. 12.
    S. Pauws and S. van de Wijdeven, “User evaluation of a new interactive playlist generation concept,” in Proceedings of 6th International Conference on Music Information Retrieval, (London, UK), pp. 638–643, 2005.Google Scholar
  13. 13.
    N. Oliver and L. Kregor-Stickles, “Papa: Physiology and purpose-aware automatic playlist generation,” in Proceedings of 7th International Conference on Music Information Retrieval, (Victoria, Canada), pp. 250–253, 2006.Google Scholar
  14. 14.
    M. Niitsuma, H. Takaesu, H. Demachi, M. Oono, and H. Saito, “Development of an automatic music selection system based on Runner’s step frequency,” in Proceedings of the 9th Conference on Music Information Retrieval, pp. 193–198, 2008.Google Scholar
  15. 15.
    M. G. Arthur Flexer, D. Schnitzer, and G. Widmer, “Playlist generation using start and end songs,” in Proceedings of the 9th Conference on Music Information Retrieval, pp. 219–224, 2008.Google Scholar
  16. 16.
    G. D. Francois Maillet, D. Eck and P. Lamere, “Steerable playlist generation by learning song similarity from radio station playlists,” in Proceedings of the 10th Conference on Music Information Retrieval, pp. 345–350, 2009.Google Scholar
  17. 17.
    K. Bosteels, E. Pampalk, and E. E. Kerre, “Evaluating and analysing dynamic playlist generation heuristics using radio logs and fuzzy set theory,” in Proceedings of the 10th Conference on Music Information Retrieval, (Kobe, Japan), pp. 351–356, 2009.Google Scholar
  18. 18.
    C. Baccigalupo, J. Donaldson, and E. Plaza, “Uncovering affinity of artists to multiple genres from social behaviour data,” in Proceedings of the 9th Conference on Music Information Retrieval, pp. 275–280, 2008.Google Scholar
  19. 19.
    P. Symeonidis, M. Ruxanda, A. Nanopoulos, and Y. Manolopoulos, “Ternary Semantic Analysis of Social Tags for Personalized Music Recommendation,” in Proceedings of the 9th Conference on Music Information Retrieval, (Philadelphia, PA), pp. 219–224, 2008.Google Scholar
  20. 20.
    J. Donaldson, “Music recommendation mapping and interface based on structural network entropy.,” in Proceedings of 8th International Conference on Music Information Retrieval, (Vienna, Austria), pp. 811–817, 2007.Google Scholar
  21. 21.
    A. Anglade, M. Tiemann, and F. Vignoli, “Virtual communities for creating shared music channels,” in Proceedings of 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007.Google Scholar
  22. 22.
    B. Fields, K. Jacobson, C. Rhodes, and M. Casey, “Social playlists and bottleneck measurements: Exploiting musician social graphs using content-based dissimilarity and pairwise maximum flow values,” in Proceedings of the 9th Conference on Music Information Retrieval, pp. 559–564, 2008.Google Scholar
  23. 23.
    D. S. Klaus Seyerlehner, P. Knees and G. Widmer, “Browsing music recommendation networks,” in Proceedings of the 10th Conference on Music Information Retrieval, (Kobe, Japan), pp. 129–134, 2009.Google Scholar
  24. 24.
    B. McFee and G. Lanckriet, “Heterogeneous embedding for subjective artist similarity,” in Proceedings of the 10th Conference on Music Information Retrieval, pp. 513–518, 2009.Google Scholar
  25. 25.
    M. Tiemann and S. Pauws, “Towards ensemble learning for hybrid music recommendation,” in Proceedings of 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007.Google Scholar
  26. 26.
    K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, “Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences,” in Proceedings of 7th International Conference on Music Information Retrieval, (Victoria, Canada), pp. 296–301, 2006.Google Scholar
  27. 27.
    K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, “Improving efficiency and scalability of model-based music recommender system based on incremental training,” in Proceedings of 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007.Google Scholar
  28. 28.
    T. Magno and C. Sable, “A comparison of signal-based music recommendation to genre labels, collaborative filtering, musicological analysis, human recommendation, and random baseline,” in Proceedings of the 9th Conference on Music Information Retrieval, (Barcelona, Spain), pp. 161–166, 2008.Google Scholar
  29. 29.
    K. Yoshii and M. Goto, “ Continuous PLSI and smoothing techniques for hybrid music recommendation,” in Proceedings of the 10th Conference on Music Information Retrieval, pp. 339–344, 2009.Google Scholar
  30. 30.
    S. J. Cunningham, D. Bainbridge, and A. Falconer, “More of an Art than a Science: Supporting the creation of playlists and mixes,” in Proceedings of 7th International Conference on Music Information Retrieval, (Victoria, Canada), pp. 240–245, 2006.Google Scholar
  31. 31.
    D. McEnnis and S. J. Cunningham, “Sociology and music recommendation systems,” in Proceedings of 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007.Google Scholar
  32. 32.
    P. Chordia, M. Godfrey, and A. Rae, “Extending content-based recommendation: The case of Indian classical music,” in Proceedings of the 9th Conference on Music Information Retrieval, pp. 571–576, 2008.Google Scholar
  33. 33.
    S. J. Cunningham and D. M. Nichols, “Exploring social music behavior: An investigation of music selection at parties,” in Proceedings of the 10th Conference on Music Information Retrieval, (Kobe, Japan), pp. 747–752, 2009.Google Scholar
  34. 34.
    L. Barrington, R. Oda, and G. Lanckriet, “Smarter than genius? Human evaluation of music recommender systems,” in Proceedings of the 10th Conference on Music Information Retrieval, (Kobe, Japan), pp. 357–362, 2009.Google Scholar
  35. 35.
    O. Celma and P. Lamere, “Music recommendation tutorial,” in Proceedings of 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007.Google Scholar
  36. 36.
    X. Hu, J. S. Downie, and A. F. Ehmann, “Exploiting recommended usage metadata: Exploratory analyses,” in Proceedings of 7th International Conference on Music Information Retrieval, (Victoria, Canada), pp. 19–22, 2006.Google Scholar
  37. 37.
    S. Pauws, W. Verhaegh, and M. Vossen, “Fast generation of optimal music playlists using local search,” in Proceedings of 7th International Conference on Music Information Retrieval, (Victoria, Canada), pp. 138–143, 2006.Google Scholar
  38. 38.
    C. Anderson, The Long Tail. Why the future of business is selling less of more. New York, NY: Hyperion, 2006.Google Scholar
  39. 39.
    N. Soundscan, “State of the industry,” Nielsen Soundscan Report. National Association of Recording Merchandisers, 2007.Google Scholar
  40. 40.
    B. Schwartz, The Paradox of Choice: Why More Is Less. Harper Perennial, January 2005.Google Scholar

Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.BMATBarcelonaSpain

Personalised recommendations