JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (3): 38-45.doi: 10.6040/j.issn.1671-9352.1.2018.149

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A modeling method of user growth profile

Zhe-jin DONG(),Jian WANG*(),Ling-fei QIAN,Hong-fei LIN   

  1. Institute of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
  • Received:2018-10-17 Online:2019-03-01 Published:2019-03-19
  • Contact: Jian WANG E-mail:zd2221@columbia.edu.cn;wangjian@dlut.edu.cn
  • Supported by:
    国家重点研发计划项目资助(2016YFB1001103)

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Abstract:

User growth value reflects users stickiness, and growth value prediction is important to accurate marketing. This paper focuses on the study of users growth portraits. For problems, disorganized raw data and unpredictable user features, this paper applies scatter diagram analysis to extract behavior features and stable time features influencing the users growth value, and compares two feature selection theories, Tree-based and L1 norm to recognize key features. For the issue of insufficient labeled training dataset, this paper improved the COREG algorithm, enriching labeled dataset through semi-supervised regression, promoting the prediction accuracy, and reducing the algorithms time complexity. Finally, this paper utilizes Stacking method to integrate different models advantages. Experiments based on the data from SMP CUP 2017, provided by the CSDN blog platform, show that the methods proposed in this paper effectively enhances models generalization ability and prediction accuracy.

Key words: user growth value, user profile, feature extraction, semi-supervised regression, ensemble method

CLC Number: 

  • TP391

Fig.1

Framework of user growth prediction"

Fig.2

Algorithm of semi-supervised model"

Fig.3

Framework of model fusion"

Table 1

User data"

数据类别 数据内容 数据量
用户内容数据 用户发表的博客 1, 000, 000篇文档
用户行为数据 用户发表博客行为 1, 000, 000条记录
用户浏览博客行为 3, 536, 444条记录
用户评论博客行为 182, 273条记录
用户对博客点赞行为 95, 668条记录
用户对博客点踩行为 9, 326条记录
用户收藏博客行为 10, 4723条记录
社交关系数据 用户之间关注关系 667, 037条记录
用户之间私信关系 46, 572条记录
成长值 2016年用户的成长值 1015条记录

Fig.4

User behavior data record"

Table 2

Prediction accuracy of models"

特征 RF SVM kNN ETR GBT
WB 0.743 0.721 0.745 0.751 0.793
L1B 0.570 0.530 0.580 0.567 0.638
TreeB 0.753 0.730 0.754 0.753 0.793
WB+WT 0.770 0.755 0.747 0.761 0.779
TreeB+WT 0.758 0.550 0.533 0.754 0.770
TreeB+TreeT 0.770 0.779 0.756 0.756 0.787
TreeB+TreeT+FkNN 0.767 0.755 0.762 0.761 0.781
TreeB+TreeT+FSVM 0.777 0.779 0.777 0.777 0.786
Stacking+FSVM 0.800

Fig.5

Scatter distribution diagram of login time and growth value"

Fig.6

Scatter plot of active months and growth values"

Fig.7

Scatter number of browsing times and growth values"

Fig.8

Scatter number of browsing times and growth values of samples added by SVM"

Table 3

Prediction scores corresponding to semi-supervised(n=1000)"

K RF SVM kNN ETR GBT
0 0.771 0.776 0.766 0.772 0.782
1 0.771 0.776 0.766 0.773 0.782
2 0.765 0.777 0.766 0.766 0.780
3 0.765 0.777 0.766 0.766 0.780
4 0.775 0.774 0.768 0.775 0.786
5 0.774 0.776 0.772 0.772 0.784
6 0.775 0.776 0.771 0.778 0.78
7 0.777 0.779 0.774 0.779 0.786
8 0.775 0.779 0.772 0.777 0.786
9 0.775 0.779 0.771 0.779 0.782
10 0.776 0.779 0.773 0.777 0.782

Fig.9

k value and accuracy distribution trend chart"

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