Exploiting Semantic Web Technology for Personalized Recommendation System

World Wide Web makes a revolution on information sharing. People could search any kinds of information from Internet and create their own web sites. However, with the increasing of information published on the Internet, the problems of redundant data, error data and incomplete data come up, which leads to the challenge of information retrieval accuracy. Users need help to search and to filter out irrelevant information based on their demand.In this Paper an approach for Semantic Recommendation system which uses Page rank based on links between Web pages, to compute relevance to user search query. It works on the assumption that a well-designed Webpage should have an informative title. The user search terms are mapped to the refined URL's extracted from the Google Server. These terms in a user query have a different degree of importance based on whether they occur inside title, description or anchor text of the document. Recommendation system has to find the relevant Web pages and prioritize them. Therefore an optimal weighting scheme for these components is required for search engines to prioritize relevant documents near the top for user searches. Personalized Recommendation System provides other user with a recommendation results for based on the common shared search queries.