Convolutional Neural Networks on Multichannel Time Series of Smartphone Applications for Gender or Age Range Classification

In this study, we developed a classification method for gender and age ranges using convolutional neural networks (CNNs). We trained an architecture using a time series of smartphone application-connecting durations and demographics of datasets containing gender and age ranges. The inputs were in the form of big data for all application categories of the time series, automatically collected from a single source panel of individuals with smartphones. The demographics of gender and age ranges were used for prediction. To identify the demographics of individuals, this study extracted effective features of mobile website access activities. We proposed a feature learning model from the raw inputs to solve the problem of classifying gender and age ranges using a deep convolutional neural network, which is an important but challenging task in digital marketing. For companies that can obtain only web application logs but not the demographics of gender and age ranges, this research may provide a possible solution that can be applied in digital marketing.