Towards Adaptive Web Sites: Conceptual Framework and Case Study. Perkowitz, M. & Etzioni, O. Artifical Intelligence, 118(1-2):245-275, April, 2000. abstract bibtex Today's Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this paper investigates adaptive Web sites: sites that automatically improve their organization and presentation by learning from visitor access patterns. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites. To demonstrate the feasibility of adaptive Web sites, the paper considers the problem of index page synthesis and sketches a solution that relies on novel clustering and conceptual clustering techniques. Our preliminary experiments show that high-quality candidate index pages can be generated automatically, and that our techniques outperform existing methods (including the Apriori algorithm, K-means clustering, hierarchical agglomerative clustering, and COBWEB) in this domain.
@article{ per00a,
author = {Mike Perkowitz and Oren Etzioni},
title = {Towards Adaptive Web Sites: Conceptual Framework and Case Study},
journal = {Artifical Intelligence},
year = {2000},
month = {April},
volume = {118},
number = {1-2},
pages = {245-275},
uri = {http://www.cs.washington.edu/research/adaptive/papers/aij.pdf},
abstract = {Today's Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this paper investigates adaptive Web sites: sites that automatically improve their organization and presentation by learning from visitor access patterns. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites. To demonstrate the feasibility of adaptive Web sites, the paper considers the problem of index page synthesis and sketches a solution that relies on novel clustering and conceptual clustering techniques. Our preliminary experiments show that high-quality candidate index pages can be generated automatically, and that our techniques outperform existing methods (including the Apriori algorithm, K-means clustering, hierarchical agglomerative clustering, and COBWEB) in this domain.}
}
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