Context-Based Prediction of App Usage

TL;DR

This paper introduces an online, device-based algorithm that predicts user app usage by learning habits related to time, location, and device state, aiming to improve app accessibility and personalization.

Contribution

It presents a novel online prediction algorithm that dynamically learns user habits for app usage, optimizing user experience on smartphones.

Findings

Algorithm maximizes AUC both theoretically and empirically.

Good prediction accuracy demonstrated on 1,000 devices.

Enhances user navigation and personalization.

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.