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Time based patterns in mobile-internet surfing

In this paper we investigate environmental factors that can result in users having different preferences and behaviors at different times of the day. An analysis is carried out of a large sample of user data for Wireless Application Protocol (WAP) browsing to determine whether user surfing patterns vary depending on time. We examine traffic on an hourly and daily basis, and show that accesses to particular categories of pages vary relative to time. We also build Markov models, which are temporal; to predict user navigation, and illustrate those predictive models are more accurate and beneficial to mobile Internet users than traditional methods. This analysis provides insight into improving the effectiveness and efficiency of navigation prediction.