  With the increasing number of mobile Apps developed, they are now closely
integrated into daily life. In this paper, we develop a framework to predict
mobile Apps that are most likely to be used regarding the current device status
of a smartphone. Such an Apps usage prediction framework is a crucial
prerequisite for fast App launching, intelligent user experience, and power
management of smartphones. By analyzing real App usage log data, we discover
two kinds of features: The Explicit Feature (EF) from sensing readings of
built-in sensors, and the Implicit Feature (IF) from App usage relations. The
IF feature is derived by constructing the proposed App Usage Graph (abbreviated
as AUG) that models App usage transitions. In light of AUG, we are able to
discover usage relations among Apps. Since users may have different usage
behaviors on their smartphones, we further propose one personalized feature
selection algorithm. We explore minimum description length (MDL) from the
training data and select those features which need less length to describe the
training data. The personalized feature selection can successfully reduce the
log size and the prediction time. Finally, we adopt the kNN classification
model to predict Apps usage. Note that through the features selected by the
proposed personalized feature selection algorithm, we only need to keep these
features, which in turn reduces the prediction time and avoids the curse of
dimensionality when using the kNN classifier. We conduct a comprehensive
experimental study based on a real mobile App usage dataset. The results
demonstrate the effectiveness of the proposed framework and show the predictive
capability for App usage prediction.
