The creation of a complex web site is a thorny problem in user interface design. In this paper we explore the notion of adaptiveweb sites: sites that semi-automatically improve their organization and presentation by learning from visitor access patterns. It is easy to imagine and implementweb sites that offer shortcuts to popular pages. Are more sophisticated adaptiveweb sites feasible? What degree of automation can weachieve? To address the questions above, we describe the design space of adaptiveweb sites and consider a case study: the problem of synthesizing new index pages that facilitate navigation of a web site. We presentthePageGather algorithm, which automatically identifies candidate link sets to include in index pages based on user access logs. We demonstrate experimentally that PageGather outperforms the Apriori data mining algorithm on this task. In addition, we compare PageGather's link sets to pre-existing, human-authored index pages.

The creation of a complex web site is a thorny problem in user interface design. In IJCAI '97, we challenged the AI community to address this problem by creating adaptive web sites: sites that automatically improve their organization and presentation by mining visitor access data collected in Web server logs. In this paper weintroduce our own approach to this broad challenge. Specifically, we investigate the problem of index page synthesis --- the automatic creation of pages that facilitate a visitor's navigation of a Web site. First, we formalize this problem as a clustering problem and introduce a novel approach to clustering, which we call cluster mining: Instead of attempting to partition the entire data space into disjoint clusters, we search for a small number of cohesive (and possibly overlapping) clusters. Next, we present PageGather, a cluster mining algorithm that takes Web server logs as input and outputs the contents of candidate index pages. Finally, we show experime...

The creation of a complex web site is a thorny problem in user interface design. In IJCAI '97, wechallenged the AI community to address this problem by creating adaptive web sites: sites that automatically improve their organization and presentation by mining visitor access data collected in Web server logs. In this paper weintroduce our own approachtothisbroadchallenge. Specifically, weinvestigate the problem of index page synthesis --- the automatic creation of pages that facilitate a visitor's navigation of a Web site. First, we formalize this problem as a clustering problem and introduce a novel approach to clustering, whichwe call cluster mining: Instead of attempting to partition the entire data space into disjoint clusters, we search forasmallnumber of cohesive (and possibly overlapping) clusters. Next, we presentPageGather, a cluster mining algorithm that takes Web server logs as input and outputs the contents of candidate index pages. Finally, weshow experime...

The creation of a complex web site is a thorny problem in user interface design. In this paper we explore the notion of adaptiveweb sites: sites that semi-automatically improve their organization and presentation by learning from visitor access patterns. It is easy to imagine and implementweb sites that offer shortcuts to popular pages. Are more sophisticated adaptiveweb sites feasible? What degree of automation can weachieve? To address the questions above, we describe the design space of adaptiveweb sites and consider a case study: the problem of synthesizing new index pages that facilitate navigation of a web site. We presentthePageGather algorithm, which automatically identifies candidate link sets to include in index pages based on user access logs. We demonstrate experimentally that PageGather outperforms the Apriori data mining algorithm on this task. In addition, we compare PageGather's link sets to pre-existing, human-authored index pages. Keywords: adaptive, cluste...