Skip to main content
Springer Nature Link
Log in

Audio Ergo Sum

A Personal Data Model for Musical Preferences

  • Conference paper
  • First Online:
Software Technologies: Applications and Foundations (STAF 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9946))

  • 917 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://www.last.fm/api/, retrieval date 2016-04-04.

  2. 2.

    Not all of them are reported due to lack of space.

  3. 3.

    The p-value is zero (or smaller than 0.000001) for all the correlations reported.

  4. 4.

    Similar results are obtained for album but they are not reported due to lack of space.

References

  1. Abiteboul, S., André, B., Kaplan, D.: Managing your digital life. Commun. ACM 58(5), 32–35 (2015)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  4. Draper, N.R., Smith, H., Pownell, E.: Applied Regression Analysis, vol. 3. Wiley, New York (1966)

    Google Scholar 

  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 2015

    Google Scholar 

  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. 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. 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. 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. 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. 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. 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)

    Article  Google Scholar 

  13. Tan, P.-N., Steinbach, M., Kumar, V., et al.: Introduction to Data Mining, vol. 1. Pearson Addison Wesley, Boston (2006)

    Google Scholar 

  14. Trasarti, R., Guidotti, R., Monreale, A., Giannotti, F.: Myway: Location prediction via mobility profiling. Inf. Syst. (2015)

    Google Scholar 

  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. 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 

Download references

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.

Author information

Authors and Affiliations

  1. KDDLab, University of Pisa, Largo B. Pontecorvo, 3, Pisa, Italy

    Riccardo Guidotti, Giulio Rossetti & Dino Pedreschi

  2. KDDLab, ISTI-CNR, Via G. Moruzzi, 1, Pisa, Italy

    Riccardo Guidotti & Giulio Rossetti

Authors
  1. Riccardo Guidotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giulio Rossetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dino Pedreschi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Riccardo Guidotti .

Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Universita di Pisa, Pisa, Italy

    Paolo Milazzo

  2. Budapest University of Technology and Economics, Budapest, Hungary

    Dániel Varró

  3. Vienna University of Technology, Vienna, Austria

    Manuel Wimmer

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Guidotti, R., Rossetti, G., Pedreschi, D. (2016). Audio Ergo Sum . In: Milazzo, P., Varró, D., Wimmer, M. (eds) Software Technologies: Applications and Foundations. STAF 2016. Lecture Notes in Computer Science(), vol 9946. Springer, Cham. https://doi.org/10.1007/978-3-319-50230-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50230-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50229-8

  • Online ISBN: 978-3-319-50230-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation