Information filtering for the newspaper

A large quantity of information is spread out to our life, especially to our cyberspace. Not only are major newspaper companies providing a Web service, so are regional companies with their characteristic Web pages. We built an information filtering system using multi-agent for the article which a user wanted to read in the regional newspaper. Our system has two main agents. One is the ranking agent for the newspaper, the other is the feedback agent. The ranking agent ranks articles based on user keywords. The feedback agent feeds back to the user profile data based on the access frequency. We propose the ranking and feedback algorithms, and estimate these algorithms by experiments. Experiments show there is relation between filtered and read article by a chi-square test of 0.05 significance level. Also they show our system is efficient for the passage of time