Location-Adapted Music Recommendation Using Tags

  • Marius Kaminskas
  • Francesco Ricci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)


Context-aware music recommender systems are capable to suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context aware recommendation task — selecting music content that fits a place of interest (POI). To address this problem we have used emotional tags attached by a users’ population to both music and POIs. Moreover, we have considered a set of similarity metrics for tagged resources to establish a match between music tracks and POIs. In order to test our hypothesis, i.e., that the users will reckon that a music track suits a POI when this track is selected by our approach, we have designed a live user experiment where subjects are repeatedly presented with POIs and a selection of music tracks, some of them matching the presented POI and some not. The results of the experiment show that there is a strong overlap between the users’ selections and the best matching music that is recommended by the system for a POI.


recommender systems location-aware context music social tagging emotions 


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  1. 1.
    Baccigalupo, C., Plaza, E.: Case-based sequential ordering of songs for playlist recommendation. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 286–300. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Cai, R., Zhang, C., Wang, C., Zhang, L., Ma, W.-Y.: Musicsense: contextual music recommendation using emotional allocation modeling. In: Proceedings of the 15th International Conference on Multimedia, pp. 553–556. ACM, New York (2007)CrossRefGoogle Scholar
  3. 3.
    Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. Journal of Information Science 32(2), 198 (2006)CrossRefGoogle Scholar
  4. 4.
    Hoashi, K., Matsumoto, K., Inoue, N.: Personalization of user profiles for content-based music retrieval based on relevance feedback. In: Proceedings of the Eleventh ACM International Conference on Multimedia, pp. 110–119. ACM, New York (2003)CrossRefGoogle Scholar
  5. 5.
    Kaminskas, M., Ricci, F.: Matching places of interest with music. In: Workshop on Exploring Musical Information Spaces, WEMIS 2009, pp. 68–73. University of Alicante (2009)Google Scholar
  6. 6.
    Kim, J.Y., Belkin, N.J.: Categories of music description and search terms and phrases used by non-music experts. In: Proceedings of the 3rd International Conference on Music Information Retrieval, Paris, France (2002)Google Scholar
  7. 7.
    Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 195–202. 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: Proceedings of the 15th International Conference on Multimedia, pp. 839–842. ACM, New York (2007)CrossRefGoogle Scholar
  10. 10.
    Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., Stumme, G.: Evaluating similarity measures for emergent semantics of social tagging. In: Proceedings of the 18th International Conference on World Wide Web, pp. 641–650. ACM, New York (2009)CrossRefGoogle Scholar
  11. 11.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. The Adaptive Web, 325–341 (2007)Google Scholar
  12. 12.
    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, pp. 25–34. ACM, New York (2006)Google Scholar
  13. 13.
    Ricci, F., Cavada, D., Mirzadeh, N., Venturini, A.: Case-based travel recommendations. In: Fesenmaier, D.R., Woeber, K.W., Werthner, H. (eds.) Destination Recommendation Systems: Behavioural Foundations and Applications, pp. 67–93. CABI (2006)Google Scholar
  14. 14.
    Schafer, J.B., Frankowski, D., Herlocker, J.L., Sen, S.: Collaborative filtering recommender systems. The Adaptive Web, 291–324 (2007)Google Scholar
  15. 15.
    Sen, S., Lam, S., Rashid, A., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F., Riedl, J.: Tagging, communities, vocabulary, evolution. In: Proceedings of the 20th Anniversary Conference on Computer Supported Cooperative Work, pp. 181–190. ACM, New York (2006)Google Scholar
  16. 16.
    Zentner, M., Grandjean, D., Scherer, K.R.: Emotions evoked by the sound of music: Characterization, classification, and measurement. Emotion 8(4), 494–521 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marius Kaminskas
    • 1
  • Francesco Ricci
    • 1
  1. 1.Free University of BolzanoBolzanoItaly

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