Mobile application usage prediction through context-based learning

Prediction for Mobile Application Usage Patterns

Advances in smartphone technology have enabled the prevalence of mobile applications. Such a variety of mobile applications make the smartphone more interesting and more humanized, and running these applications has become the major function of smartphones. However, the limited resources of current smartphone requires both researchers and companies paying more attention to the way of effectivel...

Context-Based Prediction of App Usage

There are around a hundred installed apps on an average smartphone. The high number of apps and the limited number of app icons that can be displayed on the device’s screen requires a new paradigm to address their visibility to the user. In this paper we propose a new online algorithm for dynamically predicting a set of apps that the user is likely to use. The algorithm runs on the user’s devic...

Learning Mobile App Usage Routine through Learning Automata

Since its conception, smart app market has grown exponentially. Success in the app market depends on many factors among which the quality of the app is a significant contributor, such as energy use. Nevertheless, smartphones, as a subset of mobile computing devices. inherit the limited power resource constraint. Therefore, there is a challenge of maintaining the resource while increasing the ta...

State-based context prediction in mobile systems

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Learning Device Usage in Context: