A Model based Resource Recommender System on Social Tagging Data

Web (2.0) is the place where people can upload, share and access various sources of information. Web (2.0) has given rise to information overloading problem and knowledge starvation. Recommender Systems (RS) helps in alleviating this overloading problem and gaining the exact information what we need. RS suggest user items or products based on their browsing or purchasing history. RS suggest list of items by identifying similar users with explicit user-item rating. But, in real time applications most users do not rate items. In current web (2.0) social tagging applications help us to find useritem ratings implicitly based on the user’s interest and preferences they give for the list of items. In this paper we have proposed a model based resource recommendation on social tagging information which has improved the performance of the RS. In the proposed system the topic is identified from the tagged data, based on the topic user profile is constructed by semantic approach and the recommendation is done for the user.