Open Access
E3S Web Conf.
Volume 253, 2021
2021 International Conference on Environmental and Engineering Management (EEM 2021)
Article Number 03024
Number of page(s) 16
Section Environmental Equipment Engineering Management and its Technical Application
Published online 06 May 2021
  1. Connolly, Marie & Krueger, Alan B., 2006. “Rockonomics: The Economics of Popular Music,” Handbook of the Economics of Art and Culture, in: V.A. Ginsburgh & D. Throsby (ed.), Handbook of the Economics of Art and Culture, edition 1, volume 1, chapter 20, pages 667–719, Elsevier. [Google Scholar]
  2. “Regression Analysis and Least Squares.” VRU, 29 Mar. 2018. [Google Scholar]
  3. Passman (2019), All You Need to Know About the Music Business: 10th Edition, Simon & Schuster, US. [Google Scholar]
  4. Aguiar, L. & Joel Waldfogel 2018. Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists; JRC Digital Economy Working Paper 2018-04; JRC Technical Reports, JRC112023. [Google Scholar]
  5. “Earworm.” Dictionary, Merriam-Webster. [Google Scholar]
  6. Seabrooks (2016). The Song Machine: Inside the Hit Factory. W. W. Norton & Company. [Google Scholar]
  7. Jakubowski, Kelly & Finkel, Sebastian & Stewart, Lauren & Mullensiefen, Daniel. (2016). Dissecting an Earworm: Melodic Features and Song Popularity Predict Involuntary Musical Imagery.. Psychology of Aesthetics, Creativity, and the Arts. 11. 10.1037/aca0000090. [Google Scholar]
  8. Trendjackers Team. “How Social Media Has Affected the Music Industry.” Trendjackers, 27 Jan. 2017. [Google Scholar]
  9. Nam, Juhan & Choi, Keunwoo & Lee, Jongpil & Chou, Szu-Yu & Yang, Yi-Hsuan. (2019). Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach. IEEE Signal Processing Magazine. 36. 41–51. 10.1109/MSP.2018.2874383. [Google Scholar]
  10. Lundberg, Scott & Lee, Su-In. (2017). A Unified Approach to Interpreting Model Predictions. [Google Scholar]
  11. Krueger, Alan B. “Rockonomics by Alan B. Krueger: 9781524763718: Books.”, Crown, 2019. [Google Scholar]
  12. Aguiar, L. & Martens, Bertin. 2016. Digital music consumption on the Internet: Evidence from clickstream data. 34. 27–43. 10.1016/j.infoecopol.2016.01.003. [Google Scholar]
  13. Gauvin, H.L. (2018). Drawing listener attention in popular music: Testing five musical features arising from the theory of attention economy, Musicae Scientiae, 22(3): 291–304. [CrossRef] [Google Scholar]
  14. Myra Interiano, Kamyar Kazemi, Lijia Wang, Jienian Yang, Zhaoxia Yu and Natalia L. Komarova, Musical trends and predictability of success in contemporary songs in and out of the top charts, Royal Society Open Science, 5(5):171–274. [Google Scholar]
  15. Salganik, M. & Dodds, Peter & Watts, Duncan. (2006). Experimental Study of Inequality and Unpredicatbility in an Artificial Cutlural Market. Science. 311. 854–856. [CrossRef] [PubMed] [Google Scholar]
  16. McKinney, Kelsey. “A Hit Song Is Usually 3 to 5 Minutes Long. Here's Why.” Vox, Vox, 18 Aug. 2014. [Google Scholar]
  17. Askin, Noah & Mauskapf, Michael. 2017. What Makes Popular Culture Popular?: Product Features and Optimal Differentiation in Music. American Sociological Review. 82. 10.1177/0003122417728662. [Google Scholar]
  18. Herremans, Dorien & Martens, David & Sorensen, Kenneth. 2014. Dance Hit Song Prediction, Journal of Musical Research, 43(3):291–302. [Google Scholar]
  19. Araujo, Carlos & Cristo, Marco & Giusti, Rafael. 2019. Predicting Music Popularity Using Music Charts. 859–864. 10.1109/ICMLA.2019.00149. [Google Scholar]
  20. Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825–2830, 2011. [Google Scholar]
  21. Singh, Devang. “Neural Network In Python: Introduction, Structure and Trading Strategies.” QuantInsti, QuantInsti, 27 Apr. 2020. [Google Scholar]
  22. Basheer, Imad & Hajmeer, M.N. 2001. Artificial Neural Networks: Fundamentals, Computing, Design, and Application. Journal of microbiological methods. 43. 3–31. 10.1016/S0167-7012(00)00201-3. [Google Scholar]
  23. Dewan, S., Ramaprasad, J., 2012. Music blogging, online sampling, and the long tail. Inf. Syst. Res. 23 (3-part-2), 1056–1067. DOI: 10.1287/isre.1110. 0405. [Google Scholar]
  24. Evgeny Pogorelov. “Explaining Multi-Class XGBoost Models with SHAP.” Evgeny Pogorelov, 13 May 2019. [Google Scholar]
  25. “Experience Good.” Market, [Google Scholar]
  26. “The Long Tail Theory, Debunked: We Stick With What We Know.” Mack Institute for Innovation Management, 14 Nov. 2019. [Google Scholar]
  27. Nagpal, Anuja. “Principal Component Analysis-Intro.” Medium, Towards Data Science, 22 Nov. 2017. [Google Scholar]
  28. “Sklearn.decomposition.PCA.” Scikit. [Google Scholar]
  29. Tianqi Chen, Carlos Guestrin. “XGBoost: A Scalable Tree Boosting System”. [Google Scholar]
  30. “XGBoost Documentation.” XGBoost Documentation-Xgboost 1.3.0-SNAPSHOT Documentation, [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.