Abstract
As 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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Notes
- 1.
The choice of describing a listening with these attributes is related to the case study. Additional attributes can be used when available from the data. We highlight that listening means that the song was played and not necessarily entirely listened.
- 2.
http://www.last.fm/api/, retrieval date 2016-04-04.
- 3.
The code, along with the ids of seed users used in this study, is available at https://github.com/GiulioRossetti/LastfmProfiler. The complete dataset is not released to comply with Last.fm TOS.
- 4.
The p-value is zero (or smaller than 0.000001) for all the correlations.
- 5.
The analysis of \(b_u\) have similar results (not reported due to lack of space).
- 6.
The Pearson correlations ranges in [0.96, 0.99], \(\text {p-value} \ll 1.0e^{-60}\).
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Acknowledgment
This work is partially supported by the European Community H2020 programme under the funding schemes: INFRAIA-1-2014-2015: Research Infrastructures G.A. 654024 SoBigData (http://www.sobigdata.eu), G.A. 78835 Pro-Res (http://prores-project.eu/), and G.A. 825619 AI4EU (https://www.ai4eu.eu/), and G.A. 780754 Track & Know (https://trackandknowproject.eu/).
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Guidotti, R., Rossetti, G. (2020). “Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network. In: Sekerinski, E., et al. Formal Methods. FM 2019 International Workshops. FM 2019. Lecture Notes in Computer Science(), vol 12232. Springer, Cham. https://doi.org/10.1007/978-3-030-54994-7_11
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