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Mobile application usage prediction through context-based learning

Philip Leroux (UGent) , Klaas Roobroeck (UGent) , Bart Dhoedt (UGent) , Piet Demeester (UGent) and Filip De Turck (UGent)
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Abstract
The purchase and download of new applications on all types of smartphones and tablet computers has become increasingly popular. On each mobile device, many applications are installed, often resulting in crowded icon-based interfaces. In this paper, we present a framework for the prediction of a user's future mobile application usage behavior. On the mobile device, the framework continuously monitors the user's previous use of applications together with several context parameters such as speed and location. Based on the retrieved information, the framework automatically deduces application usage patterns. These patterns define a correlation between a used application and the monitored context information or between different applications. Furthermore, by combining several context parameters, context profiles are automatically generated. These profiles typically match with real life situations such as 'at home' or 'on the train' and are used to delimit the number of possible patterns, increasing both the positive prediction rate and the scalability of the system. A concept demonstrator for Android OS was developed and the implemented algorithms were evaluated in a detailed simulation setup. It is shown that the developed algorithms perform very well with a true positive rate of up to 90% for the considered evaluation scenarios.
Keywords
SEMANTICS, ALGORITHM, Context modeling, context prediction, mobile application development, ACTIVITY RECOGNITION, RECOMMENDATIONS, IBCN

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Please use this url to cite or link to this publication:

MLA
Leroux, Philip et al. “Mobile Application Usage Prediction Through Context-based Learning.” JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 5.2 (2013): 213–235. Print.
APA
Leroux, P., Roobroeck, K., Dhoedt, B., Demeester, P., & De Turck, F. (2013). Mobile application usage prediction through context-based learning. JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, 5(2), 213–235.
Chicago author-date
Leroux, Philip, Klaas Roobroeck, Bart Dhoedt, Piet Demeester, and Filip De Turck. 2013. “Mobile Application Usage Prediction Through Context-based Learning.” Journal of Ambient Intelligence and Smart Environments 5 (2): 213–235.
Chicago author-date (all authors)
Leroux, Philip, Klaas Roobroeck, Bart Dhoedt, Piet Demeester, and Filip De Turck. 2013. “Mobile Application Usage Prediction Through Context-based Learning.” Journal of Ambient Intelligence and Smart Environments 5 (2): 213–235.
Vancouver
1.
Leroux P, Roobroeck K, Dhoedt B, Demeester P, De Turck F. Mobile application usage prediction through context-based learning. JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS. 2013;5(2):213–35.
IEEE
[1]
P. Leroux, K. Roobroeck, B. Dhoedt, P. Demeester, and F. De Turck, “Mobile application usage prediction through context-based learning,” JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, vol. 5, no. 2, pp. 213–235, 2013.
@article{4181596,
  abstract     = {The purchase and download of new applications on all types of smartphones and tablet computers has become increasingly popular. On each mobile device, many applications are installed, often resulting in crowded icon-based interfaces. In this paper, we present a framework for the prediction of a user's future mobile application usage behavior. On the mobile device, the framework continuously monitors the user's previous use of applications together with several context parameters such as speed and location. Based on the retrieved information, the framework automatically deduces application usage patterns. These patterns define a correlation between a used application and the monitored context information or between different applications. Furthermore, by combining several context parameters, context profiles are automatically generated. These profiles typically match with real life situations such as 'at home' or 'on the train' and are used to delimit the number of possible patterns, increasing both the positive prediction rate and the scalability of the system. A concept demonstrator for Android OS was developed and the implemented algorithms were evaluated in a detailed simulation setup. It is shown that the developed algorithms perform very well with a true positive rate of up to 90% for the considered evaluation scenarios.},
  author       = {Leroux, Philip and Roobroeck, Klaas and Dhoedt, Bart and Demeester, Piet and De Turck, Filip},
  issn         = {1876-1364},
  journal      = {JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS},
  keywords     = {SEMANTICS,ALGORITHM,Context modeling,context prediction,mobile application development,ACTIVITY RECOGNITION,RECOMMENDATIONS,IBCN},
  language     = {eng},
  number       = {2},
  pages        = {213--235},
  title        = {Mobile application usage prediction through context-based learning},
  url          = {http://dx.doi.org/10.3233/AIS-130199},
  volume       = {5},
  year         = {2013},
}

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