Adaptive Web Sites

Automatically Learning from User Access Patterns


Designing a web site is a complex problem. Logs of user accesses to a site provide an opportunity to observe users interacting with that site and make improvements to the site's structure and presentation. We propose adaptive sites: web sites that improve themselves by learning from user access patterns. Adaptive webs can make popular pages more accessible, highlight interesting links, connect related pages, and cluster similar documents together. An adaptive web can perform these self-improvements autonomously or advise a site's webmaster, summarizing access information and making suggestions.

In this work we define adaptive web sites, explain and formalize several kinds of improvements that an adaptive site can make, and give examples of applying these improvements to existing sites.

Adaptive Web Sites: Concept and Case Study
Perkowitz and Etzioni
In Artificial Intelligence 118(1-2), 2000
(PDF)
Towards Adaptive Web Sites: Conceptual Cluster Mining
Perkowitz and Etzioni
In IJCAI-99
(PDF)
Adaptive Web Sites: an AI Challenge
Perkowitz and Etzioni
In IJCAI-97
(PDF)
Towards Adaptive Web Sites: Conceptual Framework and Case Study
Perkowitz and Etzioni
In WWW8
(PDF)
(or HTML)
Adaptive Web Sites: Automatically Synthesizing Web Pages
Perkowitz and Etzioni
In AAAI98
(PDF)
Adaptive Sites: Automatically Learning from User Access Patterns
Perkowitz and Etzioni
A poster at WWW6
(HTML)
Adaptive Sites: Automatically Learning from User Access Patterns
Perkowitz and Etzioni
A UWCS Technical Report
(HTML)
http://www.cs.washington.edu/
(Example from the paper)
[Copy]
http://www.cs.washington.edu/education/courses/142/CurrentQtr/
(Example from the paper)
[Copy]





Mike Perkowitz and Oren Etzioni Adaptive Web Sites