Efficient mining and prediction of user behavior patterns in mobile web systems

The development of wireless and web technologies has allowed the mobile users to request various kinds of services by mobile devices at anytime and anywhere. Helping the users obtain needed information effectively is an important issue in the mobile web systems. Discovery of user behavior can highly benefit the enhancements on system performance and quality of services. Obviously, the mobile user's behavior patterns, in which the location and the service are inherently coexistent, become more complex than those of the traditional web systems. In this paper, we propose a novel data mining method, namely SMAP-Mine that can efficiently discover mobile users' sequential movement patterns associated with requested services. Moreover, the corresponding prediction strategies are also proposed. Through empirical evaluation under various simulation conditions, SMAP-Mine is shown to deliver excellent performance in terms of accuracy, execution efficiency and scalability. Meanwhile, the proposed prediction strategies are also verified to be effective in measurements of precision, hit ratio and applicability.