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Audio Ergo Sum

A Personal Data Model for Musical Preferences
  • Riccardo Guidotti
  • Giulio Rossetti
  • Dino Pedreschi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9946)

Abstract

Nobody can state “Rock is my favorite genre” or “David Bowie is my favorite artist”. We defined a Personal Listening Data Model able to capture musical preferences through indicators and patterns, and we discovered that we are all characterized by a limited set of musical preferences, but not by a unique predilection. The empowered capacity of mobile devices and their growing adoption in our everyday life is generating an enormous increment in the production of personal data such as calls, positioning, online purchases and even music listening. Musical listening is a type of data that has started receiving more attention from the scientific community as consequence of the increasing availability of rich and punctual online data sources. Starting from the listening of 30k Last.Fm users, we show how the employment of the Personal Listening Data Models can provide higher levels of self-awareness. In addition, the proposed model will enable the development of a wide range of analysis and musical services both at personal and at collective level.

Keywords

Personal Data Frequent Sequence Social Graph Musical Preference Musical Listening 
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.

Notes

Acknowledgements

This work was partially supported by the European Communitys H2020 Program under the funding scheme “INFRAIA-1-2014-2015: Research Infrastructures” grant agreement 654024 “SoBigData: Social Mining & Big Data Ecosystem”, http://www.sobigdata.eu, and under the founding scheme “FETPROACT-1-2014: Global Systems Science (GSS)”, grant agreement 641191 “CIMPLEX Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EXploratories”, https://www.cimplex-project.eu.

References

  1. 1.
    Abiteboul, S., André, B., Kaplan, D.: Managing your digital life. Commun. ACM 58(5), 32–35 (2015)CrossRefGoogle Scholar
  2. 2.
    Bischoff, K.: We love rock ‘n’ roll: analyzing and predicting friendship links in last.fm. In: Web Science 2012, WebSci 2012, Evanston, IL, USA. 22–24 June 2012, pp. 47–56 (2012)Google Scholar
  3. 3.
    de Montjoye, Y.-A., Shmueli, E., Wang, S.S., Pentland, A.S.: openPDS: protecting the privacy of metadata through safeanswers. PloS one 9(7), e98790 (2014)CrossRefGoogle Scholar
  4. 4.
    Draper, N.R., Smith, H., Pownell, E.: Applied Regression Analysis, vol. 3. Wiley, New York (1966)Google Scholar
  5. 5.
    Guidotti, R., Coscia, M., Pedreschi, D., Pennacchioli, D.: Behavioral entropy and profitability in retail. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10. IEEE (2015). 36678 2015Google Scholar
  6. 6.
    Guidotti, R., Trasarti, R., Nanni, M., Tosca: two-steps clustering algorithm for personal locations detection. In: 23rd International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2015). ACM (2015)Google Scholar
  7. 7.
    Guidotti, R., Trasarti, R., Nanni, M.: Towards user-centric data management: individual mobility analytics for collective services. In: MobiGIS Workshop Co-located with ACM SIGSPATIAL 2015. ACM (2015)Google Scholar
  8. 8.
    Keogh, E., Lonardi, S., Ratanamahatana, C.A.: Towards parameter-free data mining. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 206–215. ACM (2004)Google Scholar
  9. 9.
    Moiso, C., Minerva, R.: Towards a user-centric personal data ecosystem the role of the bank of individuals’ data. In: 2012 16th International Conference on Intelligence in Next Generation Networks (ICIN), pp. 202–209. IEEE (2012)Google Scholar
  10. 10.
    Pálovics, R., Benczúr, A.A.: Temporal influence over the last.fm social network. Social Netw. Anal. Mining 5(1), 4:1–4:12 (2015)Google Scholar
  11. 11.
    Pennacchioli, D., Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F., Coscia, M.: The three dimensions of social prominence. In: Jatowt, A., et al. (eds.) SocInfo 2013. LNCS, vol. 8238, pp. 319–332. Springer, Heidelberg (2013)Google Scholar
  12. 12.
    Putzke, J., Fischbach, K., Schoder, D., Gloor, P.A.: Cross-cultural gender differences in the adoption and usage of social media platforms - an exploratory study of last.fm. Comput. Netw. 75, 519–530 (2014)CrossRefGoogle Scholar
  13. 13.
    Tan, P.-N., Steinbach, M., Kumar, V., et al.: Introduction to Data Mining, vol. 1. Pearson Addison Wesley, Boston (2006)Google Scholar
  14. 14.
    Trasarti, R., Guidotti, R., Monreale, A., Giannotti, F.: Myway: Location prediction via mobility profiling. Inf. Syst. (2015)Google Scholar
  15. 15.
    Vescovi, M., Moiso, C., Pasolli, M., Cordin, L., Antonelli, F.: Trust management IX. In: Damsgaard Jensen, C., Marsh, S., Dimitrakos, T., Murayama, Y. (eds.) IFIPTM 2015. IAICT, vol. 454. Springer, Heidelberg (2015)Google Scholar
  16. 16.
    Vescovi, M., Perentis, C., Leonardi, C., Lepri, B., Moiso, C.: My data store: toward user awareness and control on personal data. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pp. 179–182. ACM (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Riccardo Guidotti
    • 1
    • 2
  • Giulio Rossetti
    • 1
    • 2
  • Dino Pedreschi
    • 1
  1. 1.KDDLabUniversity of PisaPisaItaly
  2. 2.KDDLabISTI-CNRPisaItaly

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