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InCarMusic: Context-Aware Music Recommendations in a Car

  • Linas Baltrunas
  • Marius Kaminskas
  • Bernd Ludwig
  • Omar Moling
  • Francesco Ricci
  • Aykan Aydin
  • Karl-Heinz Lüke
  • Roland Schwaiger
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 85)

Abstract

Context aware recommender systems (CARS) adapt to the specific situation in which the recommended item will be consumed. So, for instance, music recommendations while the user is traveling by car should take into account the current traffic condition or the driver’s mood. This requires the acquisition of ratings for items in several alternative contextual situations, to extract from this data the true dependency of the ratings on the contextual situation. In this paper, in order to simplify the in-context rating acquisition process, we consider the individual perceptions of the users about the influence of context on their decisions. We have elaborated a system design methodology where we assume that users can be requested to judge: a) if a contextual factor (e.g., the traffic state) is relevant for their decision making task, and b) how they would rate an item assuming that a certain contextual condition (e.g., traffic is chaotic) holds. Using these evaluations we show that it is possible to build an effective context-aware mobile recommender system.

Keywords

Contextual Factor Recommender System Contextual Condition Collaborative Filter Contextual Situation 
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.

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References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 217–256. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing (to appear, 2011)Google Scholar
  3. 3.
    Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: RecSys 2010: Proceedings of the 2010 ACM Conference on Recommender Systems, pp. 119–126 (2010)Google Scholar
  4. 4.
    Cai, R., Zhang, C., Wang, C., Zhang, L., Ma, W.Y.: Musicsense: contextual music recommendation using emotional allocation modeling. In: MULTIMEDIA 2007: Proceedings of the 15th International Conference on Multimedia, pp. 553–556. ACM, New York (2007)Google Scholar
  5. 5.
    Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 596–627. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2008: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM, New York (2008)Google Scholar
  7. 7.
    Koren, Y.: Collaborative filtering with temporal dynamics. In: KDD 2009: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456. ACM, New York (2009)Google Scholar
  8. 8.
    Lee, J.S., Lee, J.C.: Context awareness by case-based reasoning in a music recommendation system. In: Ichikawa, H., Cho, W.-D., Chen, Y., Youn, H.Y. (eds.) UCS 2007. LNCS, vol. 4836, pp. 45–58. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Li, C.T., Shan, M.K.: Emotion-based impressionism slideshow with automatic music accompaniment. In: MULTIMEDIA 2007: Proceedings of the 15th International Conference on Multimedia, pp. 839–842. ACM Press, New York (2007)Google Scholar
  10. 10.
    Reddy, S., Mascia, J.: Lifetrak: music in tune with your life. In: HCM 2006: Proceedings of the 1st ACM International Workshop on Human-Centered Multimedia, New York, NY, USA, pp. 25–34 (2006)Google Scholar
  11. 11.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5), 293–302 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Linas Baltrunas
    • 1
  • Marius Kaminskas
    • 1
  • Bernd Ludwig
    • 1
  • Omar Moling
    • 1
  • Francesco Ricci
    • 1
  • Aykan Aydin
    • 2
  • Karl-Heinz Lüke
    • 2
  • Roland Schwaiger
    • 2
  1. 1.Faculty of Computer ScienceFree University of Bozen-BolzanoBolzanoItaly
  2. 2.Laboratories Innovation DevelopmentDeutsche Telekom AGBerlinGermany

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