Prediction of Application Usage on Smartphones via Deep Learning

Abstract

Smartphones have proven to be a transformative tool that helps users perform various tasks such as online banking, chatting, sending an email or SMS, and online shopping. However, with the growing number of available applications and people downloading new applications at a high rate, managing the performance of such a large number of applications will increasingly become a concern, which makes managing smartphones' screens and folders complicated which led the user to spend time finding those application to perform actions such as chatting or sending a message or even finding his favorite game at evening time may take few seconds to reach. By letting the smartphone learns the user's behavior and their interactions, to predict which app the user looking for at a specific time after a certain sequence of actions. This saves users time and increases the level of usability. This paper investigates to what extent the usage of those applications can be predicted. The proposed methodology utilizes a deep learning algorithm (long short-term memory) to accurately predict the probability of a given application to be used by the smartphone user after a sequence of applications usage. The experimental result shows that forecasting those applications' usage performance can be correct with an achieved accuracy of approximately 80%.