Learning Mobile Application Usage - A Deep Learning Approach

Abstract

With more sensors embedded and functions added, mobile phones tend to be more critical to daily life. Researchers have been using the sensor data to recognize human activity these days; meanwhile, the mobile application usage prediction is also gradually brought into the spotlight. In this paper, we leveraged a state-of-the-art technique, which is LSTM, to model the mobile application usage data, also introduced a data fusion technique that eventually accomplished an over 90% of prediction accuracy. To validate the generality of our proposed solution, we applied the model on a public dataset. Our proposed solution treated the mobile application usage as a time series problem which is novel in the related field; it has the advantages of low resource consumption, short training time, as well as a generality. With the growth of users' reliance on mobile phones, mobile application usage prediction will be more useful in the future.