论文标题
基于早期采用者数据和音频功能的热门歌曲预测
Hit Song Prediction Based on Early Adopter Data and Audio Features
论文作者
论文摘要
每年音乐行业都投资了数十亿美元。这项研究为评估歌曲的打击潜力提供了一种新的策略，可以帮助唱片公司支持其投资决策。开发了许多使用音频数据和基于社交媒体听力行为的新颖功能的模型。结果表明，基于早期采用者行为的模型在预测前20名舞蹈热门时表现良好。
Billions of USD are invested in new artists and songs by the music industry every year. This research provides a new strategy for assessing the hit potential of songs, which can help record companies support their investment decisions. A number of models were developed that use both audio data, and a novel feature based on social media listening behaviour. The results show that models based on early adopter behaviour perform well when predicting top 20 dance hits.
