Adaptive Web Sites

Automatically Learning from User Access Patterns

Mike Perkowitz and Oren Etzioni

In IJCAI-97, we challenged the AI community to create adaptive web sites: web sites that automatically improve their organization and presentation by learning from user access patterns.

Although we mentioned a variety of related work in our challenge paper and even more in our talk at IJCAI, we have not been able to acknowledge every related project so far. The intention of this site is to provide references and links to work pertaining to adaptive web sites.

Adaptive Web Sites: Concept and Case Study (Our most recent paper in Artificial Intelligence 118[1-2], 2000)

Adaptive Web Sites: Conceptual Cluster Mining (Our paper in IJCAI-99)

Adaptive Web Sites: an AI Challenge (Our challenge paper in IJCAI-97)

More about our work

Download weblogs for testing! And more data!

Links to related work: WebWatcher, AiA at DFKI, Digital Information Pheromones, Web Mining

A bibliography of related work (in bibtex format).

Links to related tools: WebThreads, NetCount, I/Pro, Accrue,

Some WWW documents: Cookies, HTTP,

Mike Perkowitz and Oren Etzioni	Adaptive Web Sites