Mobile Deep Learning: Exploring Deep Neural Network for Predicting Context-Aware Smartphone Usage

Abstract

In this paper, we mainly formulate the problem of predicting smartphone usage based on contextual information, which involves both the user-centric and device-centric contexts. In the area of mobile analytics, traditional machine learning techniques, such as Decision Trees, Random Forests, Support Vector Machines, etc. are popular for building context-aware prediction models. However, real-life smartphone usage data may contain higher dimensions of contexts and can be huge in size considering the daily behavioral data of the users. Thus, the traditional machine learning models may not be effective to build the context-aware model. In this paper, we explore “Mobile Deep Learning”, an artificial neural network learning-based model considering multiple hidden layers for predicting context-aware smartphone usage. Our model first takes into account context correlation analysis to reduce the neurons as well as to simplify the network model through filtering the irrelevant or less significant contexts, and then build the deep learning model with the selected contexts. The experimental results on smartphone usage datasets show the effectiveness of the model.