Design of web page evaluation system using Ajax and neural networks

Web page evaluation is an important issue in the Internet. The page view count is a widely used criterion for the web page evaluation because of its easiness. But, the evaluation methods based on the page view count cannot reflect whether the web page content corresponds with users' needs because users click a page after looking at only the title or the small part of the page. If the page content does not satisfy a user, the user generally does not spend much time nor take any actions to look at the page so therefore we developed an Ajax Log System. Using this system, we collect users' visiting time and action on web pages such as clicks, scrolling, etc. Users are not interrupted while Ajax works. But the collected data are continuous values. We cannot determine adaptive criteria to each user data. To solve this problem, the evaluation module of the system is based on the neural network. The system with neural network learns users' action pattern while reading useful web pages and evaluates the usefulness of web pages from users' actions. Our system can more accurately find pages which satisfy users than a search engine.