A Novel Approach for Efficient and Effective Mining of Mobile User Behaviors

Abstract

Many people increasingly rely on mobile communication services to carry out daily activities. Due to the limitation of the PCS network architecture, a constantly relocating user may encounter significant delay when requesting data or value-added services. Previous research showed that this inefficiency can be effectively reduced by predicting the user's mobile patterns. However, most research merely focused on the user's moving nodes without considering the traffic times and requested services which could dramatically affect the user's behavior. The research also did not consider how likely the user is going to relocate. Thus, in this work, we extend the hidden Markov model for modeling the behavior of the mobile users with regard to the following important factors: 1) moving node, 2) requested service, 3) user state, and 4) traffic time. Our novel approach requires only one scan of the target dataset. Moreover, the needed memory space and processing time can be independent of the transaction size. A user model can be built to predict the user's mobile patterns at different granularity levels, as well as for decision support and service improvement. Moreover, the built model can be easily adjusted later to reflect the latest user behavior without re-scanning the original dataset. Our approach can also be readily used to mine streaming data.