Context-Based Prediction of App Usage
This addresses the issue of app visibility for smartphone users by providing a personalized navigation aid, though it appears incremental as it builds on existing context-based prediction methods.
The paper tackles the problem of predicting which apps a user will likely use on a smartphone by proposing an online algorithm that learns from context like time, location, and device state, showing it maximizes AUC and yields good results on 1,000 devices.
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.