Previous Bayesian classification has a problem because of reflecting semantic relation accurately in expressing characteristic of web pages. To re- solve this problem, this paper proposes automatic preference mining through learning user profile with extracted information. Apriori algorithm extracts characteristic of web pages in form of association words that reflects semantic relation and it mines association words from learning the ontological user pro- file. Our prototype personalized movie recommender system, WebBot, extracts information about movies from web pages to recommend titles based on train- ing movie set supplied by an individual user. The proposed method was tested in database that users estimated the preference about web pages, and certified that was more efficient than existent methods.
['Kyung-Yong Jung', 'Kee-Wook Rim', 'Jung-Hyun Lee']
Automatic Preference Mining through Learning User Profile with Extracted Information