“Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network

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Title“Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network
Publication TypeConference Paper
Year of Publication2020
AuthorsGuidotti, R, Rossetti, G
Secondary AuthorsSekerinski, E, Moreira, N, Oliveira, JN, Ratiu, D, Guidotti, R, Farrell, M, Luckcuck, M, Marmsoler, D, Campos, J, Astarte, T, Gonnord, L, Cerone, A, Couto, L, Dongol, B, Kutrib, M, Monteiro, P, Delmas, D
Conference NameFormal Methods. FM 2019 International Workshops
Date Published2020//
PublisherSpringer International Publishing
Conference LocationCham
ISBN Number978-3-030-54994-7
AbstractAs Nietzsche once wrote “Without music, life would be a mistake” (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.
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