Predicting Online Music Playbacks and Influencing Factors
Liu Yuanchen,Wang Hao(),Gao Yaqi
School of Information Management, Nanjing University, Nanjing 210023, China Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
[Objective] This paper predicts the amount of music playbacks and explores the influencing factors, aiming to help online music platforms evaluate the quality of music lists. [Methods] First, we used a web-crawler to retrieve the numerical and text features of music playlists from the Netease cloud. Then, we pre-trained the texts with Word2Vec and BERT. Third, we established RF, XGBoost and DNN models to predict the amount of playbacks. [Results] We found the prediction accuracy of DNN was higher than those of RF and XGBoost. The numbers of initial playbacks, comments, favorites and forwarding of music list had the most significant impacts on the amount of the music list playbacks. However, the text features reduce the prediction accuracy. [Limitations] The Netease cloud music updated everyday, therefore, we only examined the playback data collected 12 hours following the updates. [Conclusions] This study could help online music websites preliminarily judge the popularity of their music lists.
刘渊晨, 王昊, 高亚琪. 在线音乐歌单播放量预测及影响因素分析*[J]. 数据分析与知识发现, 2021, 5(8): 100-112.
Liu Yuanchen, Wang Hao, Gao Yaqi. Predicting Online Music Playbacks and Influencing Factors. Data Analysis and Knowledge Discovery, 2021, 5(8): 100-112.
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