Predicting navigation patterns on the mobile-internet using time of the week

Abstract

A predictive analysis of user navigation in the Internet is presented that exploits time-of-the-week data. Specifically, we investigate time as an environmental factor in making predictions about user navigation. An analysis is carried out of a large sample of user, navigation data (over 3.7 million sessions from 0.5 million users) in a mobile-Internet context to determine whether user surfing patterns vary depending on the time of the week on which they occur. We find that the use of time improves the predictive accuracy of navigation models.