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English Information

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Authors
# Name
1 Danilo Seufitelli(daniloboechat@dcc.ufmg.br)
2 Gabriel Oliveira(gabrielpoliveira@dcc.ufmg.br)
3 Mariana Silva(mariana.santos@dcc.ufmg.br)
4 Mirella Moro(mirella@dcc.ufmg.br)

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Reference
# Reference
1 Baccigalupo, C., Plaza, E., and Donaldson, J. (2008). Uncovering affinity of artists to multiple genres from social behaviour data. In ISMIR, pages 275–280.
2 Barbosa, G., Melo, B., Oliveira, G., Silva, M., Seufitelli, D., and Moro, M. (2021). Hot streaks in the brazilian music market: A comparison between physical and digital eras. In SBCM, pages 152–159. SBC. doi:10.5753/sbcm.2021.19440.
3 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.
4 Bertoni, A. and Lemos, R. (2021). Três datasets criados a partir de um banco de canções populares brasileiras de sucesso e não-sucesso de 2014 a 2019. In Anais do III Dataset Showcase Workshop, pages 11–20. SBC. doi:10.5753/dsw.2021.17410.
5 Bryan, N. J. and Wang, G. (2011). Musical influence network analysis and rank of sample-based music. In ISMIR, pages 329–334, Miami, USA.
6 Corrêa, D. C. and Rodrigues, F. A. (2016). A survey on symbolic data-based music genre classification. Expert Syst. Appl., 60:190–210. doi:10.1016/j.eswa.2016.04.008.
7 Cosimato, A., Prisco, R. D., Guarino, A., Malandrino, D., Lettieri, N., Sorrentino, G., and Zaccagnino, R. (2019). The conundrum of success in music: Playing it or talking about it? IEEE Access, 7:123289–123298. doi:10.1109/ACCESS.2019.2937743.
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 Oliveira, G. P. (2021). Analyses of musical success based on time, genre and collaboration. Master’s thesis, Universidade Federal de Minas Gerais, Brazil.
10 Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Botelho, J. E., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2022). Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses. Journal of Information and Data Management, 13(1). doi:10.5753/jidm.2022.2350.
11 Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., and Moro, M. M. (2021). MUHSIC: An Open Dataset with Temporal Music Success Information. In SBBD DSW, pages 65–76, Rio de Janeiro, Brazil. doi:10.5753/dsw.2021.17415.
12 Oliveira, G. P. and Moro, M. M. (2023). Exceptional collaboration patterns in music genre networks. In BraSNAM, pages 91–102. SBC. doi:10.5753/brasnam.2023.230516.
13 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 ISMIR, Montreal, Canada.
14 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.
15 Seufitelli, D. B., Oliveira, G. P., Silva, M. O., Barbosa, G. R., Melo, B. C., Botelho, J. E., de Melo-Gomes, L., and Moro, M. M. (2022). From Compact Discs to Streaming: A Comparison of Eras within the Brazilian Market. Revista Vórtex, 10(1). doi:10.33871/23179937.2022.10.1.2.
16 Seufitelli, D. B., Oliveira, G. P., Silva, M. O., and Moro, M. M. (2023). MGD+: An Enhanced Music Genre Dataset with Success-based Networks. Zenodo. doi:10.5281/zenodo.8086642.
17 Silva, A. C. M., Silva, D. F., and Marcacini, R. M. (2020). 4MuLA: A Multitask, Multimodal, and Multilingual Dataset of Music Lyrics and Audio Features. In WebMedia, page 145–148. ACM. doi:10.1145/3428658.3431089.
18 Silva, M. O., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2023). Collaboration-aware hit song prediction. Journal on Interactive Systems, 14(1):201–214. doi:10.5753/jis.2023.3137.
19 Silva, M. O., Rocha, L. M., and Moro, M. M. (2019a). Collaboration profiles and their impact on musical success. In ACM SIGAPP, page 2070–2077. ACM. doi:10.1145/3297280.3297483.
20 Silva, M. O., Rocha, L. M., and Moro, M. M. (2019b). MusicOSet: An Enhanced Open Dataset for Music Data Mining. In SBBD DSW, pages 408–417. SBC.
21 Zangerle, E., Huber, R., and Yang, M. V. Y.-H. (2019). Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In ISMIR, pages 319–326.