Smartphone User Profiling Based on Multichannel Features Extracted From Installed App List

With the rising penetration of smartphones and mobile applications (apps), mobile apps provide a perspective to interpret and profile their users. Considering that privacy concerns make it increasingly difficult to obtain rich data on users’ app usage, user profiling is implemented solely based on the list of apps installed by the user in this paper. For existing methods, most of them only conduct simple feature engineering and ignore the latent relationships that exist in the installed app list. In this paper, novel multichannel features are proposed to represent users. In addition to mining the app property, the proposed multichannel features exploit the relationships between apps, users, and user attributes. Combined with a 2‐dimensional convolutional neural network, the prediction of four user attributes is realized in this paper. Experimental results show that the proposed feature can effectively infer user attributes with the best accuracy of 79.85%, 52.34%, 69.76%, and 69.95% for gender, age, the device’s screen size, and the device’s price, which is 12.39%, 11.75%, 17.61%, and 29.23% higher than the guess based on maximum probability, respectively. Besides, the proposed prediction method has better prediction performance compared to the existing methods and can handle features with high dimensionality well.