Acquiring knowledge about user´s preferences in a Web site

Acquiring knowledge about user preferences in a Web site can be the key for continuous improvement of the site. A way to obtain it consists in understanding the user´s browsing behavior, analyzing the Web log files with data mining techniques. From the set of possible variables that contain information on the visitors behavior, two have been selected: the time spent on each visited page and the respective page content which is based on the importance of each word in a page. We propose an importance measure that considers additionally if visitors search for a specific word. With this information, a new similarity between user sessions is defined. This similarity can be applied in any clustering algorithm in order to cluster similar user sessions. Using a self-organizing feature map we cluster sessions on a certain Web site. The respective results gave very important insights regarding visitors behavior and preferences and prompted the reconfiguration of the Web site.