Collaboration as a Driving Factor for Hit Song Classification

  • Mariana O. Silva UFMG
  • Gabriel P. Oliveira UFMG
  • Danilo B. Seufitelli UFMG
  • Anisio Lacerda UFMG
  • Mirella M. Moro UFMG


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.
Palavras-chave: Hit Song Science, Hit Song Prediction, Music Information Retrieval, Music Data Mining, Machine Learning


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SILVA, Mariana O.; OLIVEIRA, Gabriel P.; SEUFITELLI, Danilo B.; LACERDA, Anisio; MORO, Mirella M.. Collaboration as a Driving Factor for Hit Song Classification. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 69-77.

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