Cited By
View all- Seufitelli DOliveira GSilva MScofield CMoro M(2023)Hit song science: a comprehensive survey and research directionsJournal of New Music Research10.1080/09298215.2023.228299952:1(41-72)Online publication date: 20-Nov-2023
Collaboration as a Driving Factor for Hit Song Classification
Authors: 
Mariana O. Silva, Gabriel P. Oliveira, Danilo B. Seufitelli, Anisio Lacerda, and 
Mirella M. MoroAuthors Info & Claims
Mariana O. Silva, Gabriel P. Oliveira, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. MoroAuthors Info & ClaimsNovember 2022
Pages 66 - 74
Abstract
The Web has transformed many services and products, including the way we consume music. In a currently streaming-oriented era, predicting hit songs is a major open issue for the music industry. Indeed, there are many efforts in finding the driving factors that shape the success of songs. Yet another feature that may improve such efforts is artistic collaboration, as it allows the songs to reach a wider audience. Therefore, we propose a multi-perspective approach that includes collaboration between artists as a factor for hit song prediction. Specifically, by combining online data from Billboard and Spotify, we model the issue as a binary classification task by using different model variants. Our results show that relying only on music-related features is not enough, whereas models that also consider collaboration features produce better results.
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Index Terms
- Collaboration as a Driving Factor for Hit Song Classification
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November 2022
389 pages
ISBN:9781450394093
DOI:10.1145/3539637
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Published: 07 November 2022
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- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
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WebMedia '22
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View all- Seufitelli DOliveira GSilva MScofield CMoro M(2023)Hit song science: a comprehensive survey and research directionsJournal of New Music Research10.1080/09298215.2023.228299952:1(41-72)Online publication date: 20-Nov-2023
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