Exploiting space and location as a design framework for interactive mobile systems
ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction with mobile systems
Multi-sensor context-awareness in mobile devices and smart artifacts
Mobile Networks and Applications
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
By their apps you shall understand them: mining large-scale patterns of mobile phone usage
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
AppJoy: personalized mobile application discovery
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Prophet: what app you wish to use next
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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As the mobile applications become increasing popular, people are installing more and more Apps on their smart phones. In this paper, we answer the question whether it is feasible to predict which App the user will open. The ability for such prediction can help pre-loading the right Apps to the memory for faster execution or help floating the desired Apps to the home screen for quicker launch. We explored a variety of contextual information, such as last used App, time, location, and the user profile, to predict the user's App usage using the MDC dataset. We present three findings from our studies. First, the contextual information can be used to learn the pattern of user's App usage and to predict App usage effectively. Second, for the MDC dataset, the correlation between sequentially used Apps has a strong contribution to the prediction accuracy. Lastly, the linear model is more effective than the Bayesian model to combine all contextual information and for such predictions.