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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (8): 100-112    DOI: 10.11925/infotech.2096-3467.2020.1013
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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
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Abstract  

[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.

Key wordsPrediction of Music List Playing Amount      Netease Cloud Music      Random Forest      XGBoost      DNN     
Received: 18 October 2020      Published: 15 September 2021
ZTFLH:  TP391  
Fund:National Social Science Fund of China(17ZDA291);Youth of Excellence in Social Sciences of Jiangsu Prince, Tang Scholar of Nanjing University
Corresponding Authors: Wang Hao ORCID:0000-0002-0131-0823     E-mail: ywhaowang@nju.edu.cn

Cite this article:

Liu Yuanchen, Wang Hao, Gao Yaqi. Predicting Online Music Playbacks and Influencing Factors. Data Analysis and Knowledge Discovery, 2021, 5(8): 100-112.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1013     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I8/100

Research Framework
类别 数字表示 类别 数字表示
怀旧 1 孤独 7
清新 2 感动 8
浪漫 3 兴奋 9
伤感 4 快乐 10
治愈 5 安静 11
放松 6 思念 12
Music List Category and Numerical Representation
Music List Play Amount Distribution
序号 字段名称 字段类型 主要内容 组合标记
1 歌单链接 字符型 用于唯一表示每个歌单(只用于标识,不作为特征选取)
2 歌单起始播放量 数值型 第一次爬取歌单信息时歌单的播放量 F1
3 歌单收藏量 数值型 歌单被多少个用户收藏,方便用户反复收听
4 歌单转发量 数值型 歌单被多少个用户转发分享至其他平台(如微信朋友圈、微博、QQ空间等)
5 歌单评论数 数值型 用户对该歌单发表的评论数
6 歌单歌曲数 数值型 歌单共收录了多少歌曲 F2
7 歌单推荐顺序 数值型 按照每个类别下歌单排列的位置顺序依次标号
8 歌单类别 数值型 12个类别,用数字1~12表示
9 歌单创建时间 数值型 将创建日期换算为距2020.5.24(即爬取数据当日)的天数
10 用户昵称 字符型 创建歌单的用户名称(只用于标识,不作为特征选取)
11 用户动态数 数值型 创建歌单的用户个人主页中发表的动态总数 F3
12 用户关注数 数值型 创建歌单的用户关注其他用户的数量
13 用户粉丝数 数值型 创建歌单的用户被其他用户关注的数量
14 用户等级 数值型 创建歌单的用户等级,一般等级越高,代表活跃度越高
15 用户创建歌单数 数值型 创建歌单的用户创建的歌单总数
16 歌单名称 文本型 由中英文、特殊符号等组成的文本数据,不允许为空值 F4
17 歌单简介 文本型 由中英文、特殊符号等组成的文本数据,通常是对歌单特征、内容的描述
18 12h后歌单播放量 数值型 间隔12h后第二次爬取时歌单的播放量,即本文进行预测的数值
Music List Data Information
参数名 参数值
n_estimators 150
max_depth 50
max_features auto
min_samples_leaf 1
bootstrap True
Parameter Setting of RF Algorithm
参数名 参数值
learning rate 0.1
n_estimators 150
max_depth 10
min_child_weight 1
gamma 0.4
Colsample_bytree 0.9
subsample 0.8
Parameter Setting of XGBoost Algorithm
隐藏层数 R2 Acc
2 0.866 0 0.745 6
3 0.871 4 0.771 2
4 0.888 0 0.815 4
5 0.888 8 0.796 8
6 0.889 0 0.844 0
7 0.880 4 0.838 0
8 0.866 5 0.803 2
The Relationship Between the Number of DNN Hidden Layers and R2, Acc
Batch_size R2 Acc
32 0.824 2 0.672 1
64 0.880 2 0.791 3
128 0.889 0 0.844 0
256 0.888 6 0.821 7
512 0.887 9 0.818 2
The Relationship Between the Batch_size of DNN and R2, Acc
The Relationship Between epoch and train_loss and val_loss
参数名 参数值
隐藏层数 6
batch_size 128
epoch 20
每层units(输出维度) 300/150/50/10/5/1
dropout 0.1
learning rate_method Adam(算法)
activation(激活函数) ReLU
Parameter Setting of DNN
Comparative Experiments with Different Algorithms
Classification of Music List Features
Comparative Experiments with Different Features of the Two Models
Comparing the Predicted Values of DNN with the True Values
R2 and Acc of Different Datasets
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