Web personalization expert with combining collaborative filtering and association rule mining technique
Web personalization has been providing electronic businesses with ways to keep existing customers and to obtain new ones. There are two approaches for providing personalized service: a content-based approach and a collaborative filtering approach. In the content-based approach, it is not easily applied to web objects (pages, images, sounds, etc) which are represented by multimedia data type information. Collaborative filtering approaches have cold-start problem. More serious weakness of collaborative filtering is that rating schemes can only be applied to homogenous domain information. In this paper, we present a framework of personalization expert by combining collaborative 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 filtering method that considers each object as an item, and attempts a personalized service. Similar users of each domain object are found as the result of the collaborative filtering method. These similar users web object access data is used by apriori algorithm to discover object association rules.
web personalization expert collaborative filtering association rule mining