Using a sizable set of sensory data and related usage records on Android devices, we are able to give a reasonable prediction of three imporant aspects of phone usage: messages, phone calls and cellular data. We solve the problem via an estimation of a user's daily routine, on which we can train a hierarchical generative model on phone usages in all time slots of a day. The model generates phone usage behaviors in terms of three kinds of data: the state of user-phone interaction, occurrence times of an activity and the duration of the activity in each occurrence. We apply the model on a dataset with 107 frequent users, and find the prediction error of generative model is the smallest when compare with several other baseline methods. In addition, CDF curves illustrate the availability of generative model for most users with the distribution of prediction error for all test cases. We also explore the effects of time slots in a day, as well as size of training and test sets. The results suggest several interesting directions for further research.
['Chuankai An', 'Dan Rockmore']
Predicting Phone Usage Behaviors with Sensory Data Using a Hierarchical Generative Model