Istituto di Scienza e Tecnologie dell'Informazione     
Guidotti R., Rossetti G., Pedreschi D. Audio ergo sum. A personal data model for musical preferences. Paolo Milazzo, Dániel Varró, Manuel Wimmer (eds.). (Lecture Notes in Computer Science, vol. 9946). Cham, Switzerland: Springer International Publishing, 2016.
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.
URL: http://link.springer.com/chapter/10.1007/978-3-319-50230-4_5
DOI: 10.1007/978-3-319-50230-4_5
Subject Music Data Mining
Personal Data Model
H.2.8 DATABASE MANAGEMENT. Database Applications. Data Mining
68W01 Computer science. General

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