An Intelligent Model for Internet Advertising Selection Based on User-Profile

With the growth of using the internet advertising, display error rate has been subsequently increased. As an instance of display error rate, it can be referred to advertisement inappropriate to user demand of modifying wrong advertising display. The most important problem related to marketing and advertising is to absolutely consider advertising true or false. To cope with such a problem, personalized advertising is made with respect to users’ profile and behavior in order that accurate internet advertising is selected, and each user receives her/his favorite internet advertising. In this study, we presented a new profile with the internet advertising in an online bookstore to students and gathered their responses. Then, we used decision tree in data mining applications and modeled two separated datasets in two states of with a profile and without a profile. The results obtained for both datasets revealed that users profile can highly influence proper classification of the internet advertising.