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.
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http://www.last.fm/api/, retrieval date 2016-04-04.
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Not all of them are reported due to lack of space.
- 3.
The p-value is zero (or smaller than 0.000001) for all the correlations reported.
- 4.
Similar results are obtained for album but they are not reported due to lack of space.
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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.
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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
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