Personalizing Results of Information Retrieval Systems Using Extended Fuzzy Concept Networks

The increasing of the electronic documents and users has led to the creation of new paradigms of personalizing results of information retrieval systems and its goal is to better service to users based on their profiles. Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. The goal of information retrieval systems is to personalize ranking documents based on user profiles. In this paper we proposed new method for personalizing results of information retrieval systems based on extended fuzzy concept networks. In this method both pages and user profiles will be showed as extended fuzzy concept networks. In an extended fuzzy concept network, there are four kinds of fuzzy relationships between concepts (1) fuzzy positive association (2) fuzzy negative association (3) fuzzy generalization (4) fuzzy specialization. An extended fuzzy concept network can be modeled by a relation matrix and a relevance matrix, where the elements in a relation matrix represent the fuzzy relationships between concepts, and the elements in a relevance matrix indicate the degrees of relevance between concepts Advantage of this method is to find the most documents with respect to the user's query and more flexible and better showing user.