量化同性恋和影响者网络对歌曲流行度预测的影响
Niklas Reisz1, Vito D P Servedio1, Stefan Thurner2,3,4
1Complexity Science Hub Vienna, Josefstädter Strasse 39, 1080, Vienna, Austria.
Scientific reports
|April 18, 2024
概括
预测歌曲的流行程度是通过了解社会联系来增强的. 音乐同恋,朋友们分享音乐品味的倾向,显著改善了歌曲受欢迎程度的预测.
科学领域:
- 音乐信息检索 音乐信息检索
- 计算社会科学 计算社会科学
- 机器学习 机器学习
背景情况:
- 预测歌曲的流行对于音乐行业来说至关重要.
- 现有的模型主要使用歌词,声学和与艺术家相关的特征.
- 音乐共享的社会动态尚未得到充分探索.
研究的目的:
- 调查音乐同性恋在预测歌曲受欢迎程度方面的作用.
- 开发基于社交网络分析的预测影响参数.
- 增强现有的机器学习模型用于歌曲受欢迎程度预测.
主要方法:
- 利用了来自last.fm在线音乐平台的广泛数据集.
- 提取了用户之间的社交联系和倾听模式.
- 设计了一种预测影响参数,结合了音乐同类感.
- 将影响参数集成到最先进的机器学习模型中.
主要成果:
- 包括基于音乐同性恋的影响参数显著提高了预测准确性.
- 预测精度提高了大约50% (从0.14到0.21).
- 社交网络效应被证明与艺术家的受欢迎程度或类型一样重要.
结论:
- 音乐同恋是歌曲受欢迎程度的强大预测因素.
- 社交网络分析为音乐人气预测提供了一个有价值的维度.
- 未来的研究应该整合社会因素,以获得更强大的预测模型.
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