| dc.creator | Soares Araujo, Carlos Vicente | |
| dc.creator | Pinheiro de Cristo, Marco Antônio | |
| dc.creator | Giusti, Rafael | |
| dc.date | 2020-12-23 | |
| dc.date.accessioned | 2022-10-04T22:27:46Z | |
| dc.date.available | 2022-10-04T22:27:46Z | |
| dc.identifier | https://seer.ufrgs.br/index.php/rita/article/view/RITA_VOL27_NR4_108 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/3870436 | |
| dc.description | The global music market moves billions of dollars every year, most of which comes from streamingplatforms. In this paper, we present a model for predicting whether or not a song will appear in Spotify’s Top 50, a ranking of the 50 most popular songs in Spotify, which is one of today’s biggest streaming services. To make this prediction, we trained different classifiers with information from audio features from songs that appeared in this ranking between November 2018 and January 2019. When tested with data from June and July 2019, an SVM classifier with RBF kernel obtained accuracy, precision, and AUC above 80%. | en-US |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Instituto de Informática - Universidade Federal do Rio Grande do Sul | en-US |
| dc.relation | https://seer.ufrgs.br/index.php/rita/article/view/RITA_VOL27_NR4_108/pdf | |
| dc.rights | Copyright (c) 2020 Carlos Vicente Soares Araujo, Marco Antônio Pinheiro de Cristo, Rafael Giusti | pt-BR |
| dc.source | Revista de Informática Teórica e Aplicada; Vol. 27 No. 4 (2020); 108-117 | en-US |
| dc.source | Revista de Informática Teórica e Aplicada; v. 27 n. 4 (2020); 108-117 | pt-BR |
| dc.source | 2175-2745 | |
| dc.source | 0103-4308 | |
| dc.subject | Music | en-US |
| dc.subject | Hit Song Science | en-US |
| dc.subject | Machine Learning | en-US |
| dc.subject | Spotify | en-US |
| dc.title | A Model for Predicting Music Popularity on Streaming Platforms | en-US |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
