Audio Ergo Sum

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


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.


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.



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”,, 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”,


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

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

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