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arXiv · 1512.07851

Context-Based Prediction of App Usage

Joseph Keshet·Adam Kariv·Arnon Dagan·Dvir Volk·Joey Simhon
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Abstract

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.

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Joseph Keshet, Adam Kariv, Arnon Dagan, Dvir Volk, Joey Simhon. 2016-01-25. Context-Based Prediction of App Usage. https://arxiv.org/abs/1512.07851

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