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English Information

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Authors
# Name
1 Gabriel Oliveira(gabrielpoliveira@dcc.ufmg.br)
2 Gabriel Barbosa(grgb@ufmg.br,)
3 Bruna Melo(brunacamposmelo@dcc.ufmg.br)
4 Mariana Silva(mariana.santos)
5 Danilo Seufitelli(daniloboechat@dcc.ufmg.br)
6 Mirella Moro(mirella@dcc.ufmg.br)

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Reference
# Reference
1 Aggarwal, C. C. (2016). Recommender Systems - The Textbook. Springer. doi:10.1007/978-3-319-29659-3.
2 Bertin-Mahieux, T. et al. (2011). The Million Song Dataset. In Proc. of Int’l Society for Music Information Retrieval Conf. (ISMIR), pages 591–596.
3 Byrd, D. and Crawford, T. (2002). Problems of music information retrieval in the real world. Information Processing & Management, 38(2):249–272. doi:10.1016/S0306-4573(01)00033-4.
4 Cosimato, A. et al. (2019). The conundrum of success in music: Playing it or talking about it? IEEE Access, 7:123289–123298. doi:10.1109/ACCESS.2019.2937743
5 Çimen, A. and Kayis, E. (2021). A longitudinal model for song popularity prediction. In DATA, pages 96–104. SciTePress. doi:10.5220/0010607700960104
6 Garimella, K. and West, R. (2019). Hot streaks on social media. In International Conference on Web and Social Media, pages 170–180. AAAI Press.
7 Janosov, M., Battiston, F., and Sinatra, R. (2020). Success and luck in creative careers. EPJ Data Sci., 9(1):9. doi:10.1140/epjds/s13688-020-00227-w.
8 Karydis, I., Gkiokas, A., and Katsouros, V. (2016). Musical track popularity mining dataset. In IFIP AIAI, pages 562–572. doi:10.1007/978-3-319-44944-9 50
9 Keogh, E. J. and Pazzani, M. J. (2000). Scaling up dynamic time warping for datamining applications. In ACM SIGKDD, pages 285–289. ACM. doi:10.1145/347090.347153
10 Liu, L., Wang, Y., Sinatra, R., Giles, C. L., Song, C., and Wang, D. (2018). Hot streaks in artistic, cultural, and scientific careers. Nature, 559(7714):396–399. doi:10.1038/s41586-018-0315-8
11 Melchiorre, A. B. et al. (2021). Investigating gender fairness of recommendation algorithms in the music domain. Information Processing & Management, 58(5):102666. doi:10.1016/j.ipm.2021.102666
12 Oliveira, G. P. (2021). Analyses of musical success based on time, genre and collaboration. Master’s thesis, Universidade Federal de Minas Gerais, Brazil.
13 Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2021). MUHSIC: An Open Dataset with Temporal Musical Success Information. Zenodo. doi:10.5281/zenodo.5168695.
14 Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020). Detecting collaboration profiles in success-based music genre networks. In Procs. Int’l Society for Music Information Retrieval Conference (ISMIR), Montreal, Canada.
15 Pachet, F. (2011). Hit song science. In Tao Li, Mitsunori Ogihara, G. T., editor, Music Data Mining, chapter 10, pages 305–326. CRC Press, New York, NY, USA.
16 Scaringella, N., Zoia, G., and Mlynek, D. (2006). Automatic genre classification of music content: a survey. IEEE Signal Process. Mag., 23(2):133–141. doi:10.1109/MSP.2006.1598089.
17 Silva, M. O., Rocha, L. M., and Moro, M. M. (2019). MusicOSet: An Enhanced Open Dataset for Music Data Mining. In SBBD DSW, pages 408–417. SBC.
18 Zangerle, E., Huber, R., and Yang, M. V. Y.-H. (2019). Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In Proc. of Int’l Society for Music Information Retrieval Conf. (ISMIR), pages 319–326.