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Journal of Applied Sciences
Year: 2004  |  Volume: 4  |  Issue: 1  |  Page No.: 118 - 125

Personalized Advertising Recommend Mechanism for the Mobile User

Kuo-Qin Yan, Shu-Ching Wang and Chia-Hui Wei    

Abstract: Due to the internationalization of the domestic business environment nowadays, competitions that every company has to survive have come not merely from the challenges of other local companies but from everywhere around the world. In order to support high quality service to contribute the most to business interest so that companies can stay highly competitive, optimized, standardized and flexible advertising recommend mechanism must be developed. With a view to exploring how to digitally turn the customer transaction information into real value for business organizations, in this study, we shall focus on the establishment of mobile advertising recommend mechanism. In this paper, we propose a two level personalized mobile advertising recommend mechanism. The method of Genetic Algorithm (GA) is used at first and then the method of Back Propagation Network (BPN) is used to extract the customer characteristic information and increase the accuracy in the learning rate. To start with using e-ticket shopping that it raises security while add cryptography, then employ mobile agent will collect and merge the information of e-ticket transaction records and personal local site. On these grounds, the proposed model can reduce the cost of manpower, promote the service quality and performance and offer automatically proper solution to increase the customer satisfaction

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