Data mining approach for user profile generation on advertisement serving

Advertisement serving on website is a prosperous business with huge market and millions of dollar prospect. By placing right advertisement at right time and place to right people, advertiser can increase their revenue by huge margin. The question is how advertiser and broker can push the right advertisement to the right user. User profiling can be used to analyze user's behavior and predict what kind of advertisement should be served to the website user. Data mining approach can be harnessed to help with user profiling process. With data mining technique, user's trace can be used as data source for behavior analysis. This research is used to do user profiling based on their browsing history stored on proxy server. Their browsing history serves as the basis of content crawling for content analysis using Multinomial Naïve Bayes classifier based text classification. The result of profiling then will be used as the basis for serving advertisement to user. The result of content analysis is validated by asking user's preferences and comparing it with profile generated by classifier engine.