Collaboration as a Driving Factor for Hit Song Classification

Mariana O. Silva, Gabriel P. Oliveira, Danilo B. Seufitelli, A. Lacerda, M. Moro
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引用次数: 5

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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合作是热门歌曲分类的驱动因素
网络已经改变了许多服务和产品,包括我们消费音乐的方式。在当前以流媒体为导向的时代,预测热门歌曲是音乐行业面临的一个重大问题。事实上,人们在寻找影响歌曲成功的驱动因素方面做了很多努力。然而,另一个可能改善这种努力的特点是艺术合作,因为它可以让歌曲接触到更广泛的听众。因此,我们提出了一种多视角的方法,将艺术家之间的合作作为热门歌曲预测的一个因素。具体来说,通过结合Billboard和Spotify的在线数据,我们通过使用不同的模型变体将问题建模为二元分类任务。我们的研究结果表明,仅仅依赖与音乐相关的特征是不够的,而同时考虑协作特征的模型会产生更好的结果。
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