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2017 | OriginalPaper | Buchkapitel

App Uninstalls Prediction: A Machine Learning and Time Series Mining Approach

verfasst von : Jiaxing Shang, Jinghao Wang, Ge Liu, Hongchun Wu, Shangbo Zhou, Yong Feng

Erschienen in: Neural Information Processing

Verlag: Springer International Publishing

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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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Vorheriges Kapitel TMRCP: A Trend-Matching Resources Coupled Prediction Method over Data Stream

Nächstes Kapitel Time Series Forecasting Using GRU Neural Network with Multi-lag After Decomposition

http://www.appchina.com/.

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Titel: App Uninstalls Prediction: A Machine Learning and Time Series Mining Approach
verfasst von: Jiaxing Shang
Jinghao Wang
Ge Liu
Hongchun Wu
Shangbo Zhou
Yong Feng
Verlag: Springer International Publishing
Buch: Neural Information Processing
Print ISBN: 978-3-319-70138-7

Electronic ISBN: 978-3-319-70139-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-70139-4_52

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