Automated Spatiotemporal Classification Based on Smartphone App Logs

Abstract

In this paper, a framework for user app behavior analysis using an automated supervised learning method in smartphone environments is proposed. This framework exploits the collective location data of users and their smartphone app logs. Based on these two datasets, the framework determines the apps with a high probability of usage in a geographic area. The framework extracts the app-usage behavior data of a mobile user from an Android phone and transmits them to a server. The server learns the representative trajectory patterns of the user by combining the collected app usage patterns and trajectory data. The proposed method performs supervised learning with automated labeled trajectory data using the user app data. Furthermore, it uses the behavioral characteristics data of users linked to the app usage data by area without a labeling cost.