Web personalization expert with combining collaborative filtering and association rule mining technique

Web personalization has been providing electronic businesses with ways to k eep existing customers and to obtain new ones. There are two approaches for providing personalized service: a content-based approach and a collaborati ve filtering approach. In the content-based approach, it is not easily appl ied to web objects (pages, images, sounds, etc) which are represented by mu ltimedia data type information. Collaborative filtering approaches have col d-start problem. More serious weakness of collaborative filtering is that r ating schemes can only be applied to homogenous domain information. In this paper, we present a framework of personalization expert by combining colla borative filtering method and association rule mining technique to overcome problems that traditional personalized systems have. Since multimedia data type web object cannot be easily analyzed, we adopted a collaborative filt ering method that considers each object as an item, and attempts a personal ized service. Similar users of each domain object are found as the result o f the collaborative filtering method. These similar users' web object acces s data is used by apriori algorithm to discover object association rules. ( C) 2001 Elsevier Science Ltd. All rights reserved.