1. Wikström, P. The music industry: music in the cloud: Digital media and society series. The music industry / P. Wikström. - Cambridge; Malden, MA: Polity, 2009. - 204 p.
2. IFPI Global Music Report 2024 // IFPI, 2024. - URL: https://ifpi-website-cms.s3.eu-west-2.amazonaws.com/IFPI_GMR_2024_State_of_the_Industry_db92a1c9c1.pdf.
3. Vaccaro, V. L. The Evolution of Business Models and Marketing Strategies in the Music Industry / V. L. Vaccaro, D. Y. Cohn // International Journal on Media Management. - 2004. - Vol. 6, Issue 1-2. - P. 46-58. DOI: 10.1080/14241277.2004.9669381
4. McKenzie, J. Digital piracy /j. McKenzie. // Handbook of Cultural Economics, Third Edition / eds. R. Towse, T. Navarrete Hernández. - Edward Elgar Publishing, 2020. DOI: 10.4337/9781788975803.00031
5. Farchy, J. D. Artificial intelligence /j. Farchy, J. Denis // Handbook of Cultural Economics, Third Edition / eds. R. Towse, T. Navarrete Hernández. - Edward Elgar Publishing, 2020. DOI: 10.4337/9781788975803.00010
6. Grote, F. Mark Mulligan: Awakening. The Music Industry in the Digital Age. London (MIDiA Research) 2015, 332 Seiten / F. Grote. // Zeitschrift für Kulturmanagement. - 2016. - Vol. 2. - P. 177-182. DOI: 10.14361/zkmm-2016-0214
7. Aguiar, L. Platforms, Power, and Promotion: Evidence from Spotify Playlists / L. Aguiar, J. Waldfogel // The Journal of Industrial Economics. - 2021. - Vol. 69, Issue 3. - P. 653-691. DOI: 10.1111/joie.12263 EDN: TYWKVB
8. Hirsch. The Structure of the Popular Music Industry: The Filtering Process by which Records are Preselected for Public Consumption / Hirsch // Institute for Social Research, The University of Michigan. - 1973.
9. Ordanini, A. Selection models in the music industry: How a prior independent experience may affect chart success / A. Ordanini // Journal of Cultural Economics. - 2006. - Vol. 30. - P. 183-200. DOI: 10.1007/s10824-006-9013-8 EDN: DUQNRR
10. Morris, J. W. Control, curation and musical experience in streaming music services /j. W. Morris, D. Powers // Creative Industries Journal. - 2015. - Vol. 8, Issue 2. - P. 106-122. DOI: 10.1080/17510694.2015.1090222
11. Analyzing the Spotify Top 200 Through a Point Process Lens / M. Harris, B. Liu, C. Park [et al.] // arXiv. - 2019. DOI: 10.48550/arXiv.1910.01445
12. Kaimann, D. “I will survive”: Online streaming and the chart survival of music tracks / D. Kaimann, I. Tanneberg, J. Cox. // Managerial and Decision Economics. - 2021. - Vol. 42, Issue 1. - P. 3-20. DOI: 10.1002/mde.3226 EDN: GXDQEM
13. An application of the Lotka-Volterra model with time series analysis to forecast spotify streams of two genres /j. R. F. Padilla, E. D. Baniaga, R. C. Addawe, J. P. T. Viernes. // Proceedings of the International Conference on Mathematical Sciences and Technology 2022 (MATHTECH 2022): Navigating the Everchanging Norm with Mathematics and Technology. 13-15 September 2022 Penang, Malaysia, 2024. - P. 070001. DOI: 10.1063/5.0192499
14. Music popular trends prediction based on time series / Yu W. [et al.] // Computer Engineering & Science. - 2018. - Vol. 40, Issue 9. - P. 1703-1709. (in Chinese.) - URL: http://joces.nudt.edu.cn/CN/abstract/abstract15746.shtml.
15. LSTM-RPA: A Simple but Effective Long Sequence Prediction Algorithm for Music Popularity Prediction. LSTM-RPA / K. Li, M. Li, Y. Li, M. Lin // arXiv. - 2021. -. DOI: 10.48550/arXiv.2110.15790