Learning user interest model for content-based filtering in personalized recommendation system

With the emergence and evolution of Networks, the information on the Internet has increased greatly. Retrieving useful information from a large amount of information has become a key technology in the information area. The application of personalized recommendation in the Internet effectively improved its service, especially the service of E-commerce. Traditional search engine do not take different user's interest into consideration, so the result they retrieved cannot satisfy user's specified needs. In order to effectively solve the problem, this paper presented a personalized recommendation system employing user interest model for content-based filtering. This paper analyzes the system of five different components: document information extraction, document vectors representation, user interest model representation; matching algorithms, user feedback update. This personalized recommendation system can describe user's interest type and interest degree well, and can enhance the personalized information service efficiency.