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.
@article{arxiv.1512.07851,
title = {Context-Based Prediction of App Usage},
author = {Joseph Keshet and Adam Kariv and Arnon Dagan and Dvir Volk and Joey Simhon},
journal= {arXiv preprint arXiv:1512.07851},
year = {2016}
}