A Multi-Agent Based Personalized Meta-Search Engine Using Automatic Fuzzy Concept Networks

As a result of the rapid growth and dynamic content of the web, the general purpose web search engines are becoming deficient. Although the meta-search engines can help by increasing the search coverage of the web, the large number of irrelevant results returned by a meta-search engine is still causing problems for the users. The personalization of meta-search engines overcomes this problem by filtering results respect to individual user's interests. In this paper, a multi-agent architecture is introduced for personalizing meta-search engine using the fuzzy concept networks. The main goal of this paper is to use automatic fuzzy concept networks to personalize results of a meta-search engine provided with a multi-agent architecture for searching and quickly retrieving. Experimental results indicate that the personalized meta-search results of the system are more relevant than the combined results of the search engines.