A new generation method of a user profile for information filtering on the Internet

It is very difficult for search engines to find adequate information, which reflects user´s interests that are changing with time on Internet. We need various learning techniques to know the user´s interests with only given simple query, and to make adaptive Information Retrieval Agents. In this paper, we propose a learning mechanism that can be used in making it easier to construct the user profile from accepted relevant documents´ terms. These documents are proposed by the user´s relevance feedback and we select key-terms in the documents for making profile. With using this profile we can filter the retrieved information and represent more relevant information to user. This approach has applied to an autonomous information gathering and filtering system, named IGIMA (Intelligent Information Gathering and Filtering System based on Multi-Agent)