Mobile Commerce

Advances in wireless technology increase the number of mobile device users and give pace to the rapid development of mobile commerce conducted with these devices. Empowered by the Webs interactive and quick-response capabilities, one-to-one mobile marketing is a very promising direct marketing channel. However, the success of mobile commerce largely depends on whether personalization can be utilized to deliver highly personalized and context sensitive information to mobile clients. To realize the personalization function in mobile advertising, Bayesian Network-based user-modeling technique is adopted. We propose that context, content, and user preferences are the important components that can be utilized to achieve personalization effect in mobile advertising application. The collected data of user information from the empirical study is used as prior probabilities for the Bayesian network. The personalized Bayesian Network-based prototype consists of a user model, a context model, a content component and a matching engine to deliver more relevant advertisements to customers through mobile devices.