• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1163-1176
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• 2005

The Web is the largest distributed information space but, the individual's capacity to read and digest contents is essentially fixed. 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 extend a family of novel algorithms (termed WebPR - Web Page Recommend) for mining frequent traversal patterns and then pageset to recommend. We add a WebPR(A) algorithm into a family of WebPR algorithms, and propose a new winWebPR(T) algorithm introducing a window concept on WebPR(T). Including two extended algorithms, our experimentation with two real data sets, including LadyAsiana and KBS media server site, clearly validates that our method outperforms conventional methods.