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TrCMP: An App Usage Inference Method for Mobile Service Enhancement

  • Xuan Zhao
  • Md Zakirul Alam Bhuiyan
  • Lianyong Qi
  • Hongli Nie
  • Wajid Rafique
  • Wanchun Dou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11342)

Abstract

In order to improve the quality of life and the efficiency of work, users need timely and accurate services provided by mobile devices. However, for the same service, different users have various personalized use styles, such as usage time, invoking frequency, etc. As a result, the accuracy of real-time service recommendations often depends on effective user behavior analysis. Technically, user behaviors associated with a certain service could be reflected with traffic, CPU, memory and energy consumption during app running. In this paper, an app usage inference method, named TrCMP, is investigated. This method takes Traffic, CPU, Memory and Power into consideration in a comprehensive way for analyzing user behaviors. Extensive experiments are conducted to validate the efficiency and effectiveness of our method.

Keywords

App usage Behavior Service enhancement Android 

Notes

Acknowledgments

This work is supported in part by the National Science Foundation of China under Grant No. 61672276, the National Key Research and Development Program of China under Grant No. 2017YFB1400600, and the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xuan Zhao
    • 1
  • Md Zakirul Alam Bhuiyan
    • 2
  • Lianyong Qi
    • 3
  • Hongli Nie
    • 1
  • Wajid Rafique
    • 1
  • Wanchun Dou
    • 1
  1. 1.State Key Laboratory for Novel Software Technology, Department of Computer Science and TechnologyNanjing UniversityNanjingChina
  2. 2.Department of Computer and Information SciencesFordham UniversityNew YorkUSA
  3. 3.School of Information Science and EngineeringQufu Normal UniversityQufuChina

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