Customising WAP-based information services on mobile networks

In addition to voice transmission over mobile networks, the demand of data communication has been increasing. To deploy data-oriented applications for mobile terminals, the wireless application protocol (WAP) has provided a promising solution. However, as in the World Wide Web (WWW), the increasing information leads to the problem of information overload. One way to overcome such a problem is to build intelligent recommender systems to provide customised information services. By analyzing the information collected from the user, a customised recommender system is able to reason his personal preferences and to build a model of predictions. In this way, only the information predicted as user-interested can reach the end user. This paper presents a multi-agent framework in which a decision tree-based approach is employed to learn a user's preferences. To assess the proposed framework, a mobile phone simulator is used to represent a mobile environment and a series of experiments are conducted. The experimental studies have concentrated on how to recommend appropriate information to the individual user, and on how the system can adapt to a user's most recent preferences. The results and analysis show that based on our framework the WAP-based customised information services can be successfully performed.