Yun Seon Hui , Kim Sam Geun , Lee Chang Hun

The KIPS Transactions:PartB , Vol. 11, No. 2, pp. 187-198, Apr. 2004
10.3745/KIPSTB.2004.11.2.187,   PDF Download:

Abstract

The World-Wide Web is the largest distributed information space and has grown to encompass diverse information resources. However, although Web is growing exponentially, the individual´s capacity to read and digest contents is essentially fixed. From the view point of Web users, they can be confused by explosion of Web information, by constantly changing Web environments, and by lack of understanding needs of Web users. In these Web environments, mining traversal patterns is an important problem in Web mining with a host of application domains including system design and information services. Conventional traversal pattern mining systems use the inter-pages association in sessions with only a very restricted mechanism (based on vector or matrix) for generating frequent k-Pagesets. We develop a family of novel algorithms (termed WebPR-Web Page Recommend) for mining frequent traversal patterns and then pageset to recommend. Our algorithms provide Web users with new page views, which include pagesets to recommend, so that users can effectively traverse its Web site. The main distinguishing factors are both a point consistently spanning schemes applying inter-pages association for mining frequent traversal patterns and a point proposing the most efficient tree model. Our experimentation with two real data sets, including LadyAsiana and KBS media server site, clearly validates that our method outperforms conventional methods.

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