Customer Personalized System of eCRM Using Web Log Mining and Rough Set

In this paper, we propose a customer personalized system that presents the web pages to users which are customized to their individuality. It analyzes the action of users who visit the shopping mall, and preferentially supplies the necessary information to them. When they actually buy some items, it forecasts the users' access pattern to web site and their following purchasable items and improves their web pare on the bases of their individuality. It reasons the relation among the web documents and among the items by using the log data of web server and the purchase information of DB. For reasoning it employs Rough Set, which is a method that searches the association rule and offers most suitable cases by reduces cases. It reasons the web pages by considering the users' access pattern and time by using the web log and reasons the users' purchase pattern by using the purchase information of DB. On the basis of the relation among them, it appends the related web pages to link of users' web pages and displays the inferred goods on users' web pages.