  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 device and constantly learns the user's habits at a given time,
location, and device state. It is designed to actively help the user to
navigate to the desired app as well as to provide a personalized feeling, and
hence is aimed at maximizing the AUC. We show both theoretically and
empirically that the algorithm maximizes the AUC, and yields good results on a
set of 1,000 devices.
