Abstract
Nowadays mobile applications (a.k.a. app) are playing unprecedented important roles in our daily life and their research has attracted many scholars. However, traditional research mainly focuses on mining app usage patterns or making app recommendations, little attention is paid to the study of app uninstall behaviors. In this paper, we study the problem of app uninstalls prediction based on a machine learning and time series mining approach. Our approach consists of two steps: (1) feature construction and (2) model training. In the first step we extract features from the dynamic app usage data with a time series mining algorithm. In the second step we train classifiers with the extracted features and use them to predict whether a user will uninstall an app in the near future. We conduct experiments on the data collected from AppChina, a leading Android app marketplace in China. Results show that the features mined from time series data can significantly improve the prediction performance.
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Acknowledgements
This work was supported in part by National Natural Science Foundation of China (No. 61702059), China Postdoctoral Science Foundation (No. 2017M612913), Fundamental Research Funds for the Central Universities of China (No. 106112016CDJXY180003), Graduate Student Research and Innovation Foundation of Chongqing City (No. CYS17024), Frontier and Application Foundation Research Program of Chongqing City (No. cstc2017jcyjAX0340, cstc2015jcyjA40006), Social Undertakings and Livelihood Security Science and Technology Innovation Funds of Chongqing City (No. cstc2017shmsA20013).
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Shang, J., Wang, J., Liu, G., Wu, H., Zhou, S., Feng, Y. (2017). App Uninstalls Prediction: A Machine Learning and Time Series Mining Approach. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_52
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