Research Statement.
The focus of my research has been applications of machine learning
and data mining to user interfaces, particularly on the World
Wide Web. The deep problems of artificial intelligence lead to
fascinating research; and if we are still far away from true
intelligence, AI has many more immediate applications. My own
interest is in improving human-computer interaction; I would like to make
computers easier to use by making them somewhat more intelligent.
Although I focus on the "more intelligent" rather than the
"easier to use" aspect, I believe that computers and the Internet
could and should be useable by and accessible to everyone.
My research, then, has tended to focus on making computers more
responsive or more helpful in their interactions with people, from
interacting with information resources on our behalf to
improving the presentation of a web site based on how people use it.
Techniques from machine learning and data mining, I believe, are
especially applicable to this goal. Especially in web applications,
there is great potential to learn from the way users interact
with software.
As an undergraduate, I studied cognitive science at Brown University,
with emphasis on linguistics and artificial intelligence. My honors thesis
-- advised by Leslie Kaelbling and Mark Johnson -- was on reinforcement
learning for simple agents in a simulated non-Markovian domain.
While there I also did some work with Eugene Charniak and several other
students on statistical language processing. When I came to the
University of Washington, I began working with Oren Etzioni.
The Internet Learning Agent.
The Internet Learning Agent (ILA) learns how to understand Internet
information resources (such as phone books) by interacting with them.
Starting with a small
amount of knowledge, ILA can bootstrap itself to understanding a number
of information sources. We published a paper on ILA in IJCAI '95:
Category Translation: Learning to
Understand Information on the Internet [1]. My work on ILA was
also my masters thesis in 1995. ILA was a part of the Internet Softbot,
an intelligent agent for the internet; users would tell the softbot
what to do (e.g., finding someone's phone number), and the softbot
would figure out how to do it (e.g., searching online phonebooks).
ILA's ability to learn how to use new information sources would make
the softbot capable of expanding its abilities automatically.
Automatically learning to interact with information resources is
a complex problem and ILA tackled only part of it -- translating
the output of such a resource into familiar concepts.
The ShopBot, in a sense, addressed another portion of this problem --
discovering information resources and learning how to communicate
with them. The authors of ILA and of the ShopBot (myself, Robert
Doorenbos, Oren Etzioni, and Dan Weld) wrote an article giving
an overview of this problem and discussing ILA and the ShopBot
in that context. Learning to Understand
Information on the Internet: an Example-Based Approach[2]
appeared in the Journal of Intelligent Internet Systems in 1997.
Web Browsing and Sharing.
Our next project was Clio, an approach to allowing a person to browse
her own web history through a dialogue with an intelligent assistant.
The user can say things like "Show me that page about cars I was looking
at last week" and the system will find the page, possibly narrowing
down the choices by asking questions such as "Do you mean the one
about sports cars or the one about sedans?". Unfortunately, though
we had many ideas and several implementations, we never published any
findings.
I spent the summer of 1996 as an intern at Microsoft Research
with the User Interfaces Group. While there, I worked on an idea for categorizing and sharing one's web
history through interaction with characters. Characters had
distinct personalities and kept track of their own favorite web pages
and could make recommendations to users. Multiple users shared access
to a collection of characters, allowing page references to be shared
among users filtered through the characters. I presented this work at
the Lifelike Computer Characters conference in 1996.
Adaptive Web Sites.
Designing a complex web site so that it readily yields its
information is a difficult task. The designer must anticipate the users'
needs and structure the site accordingly. Yet users may have vastly
differing views of the site's information, their needs may change over
time, and their usage patterns may violate the designer's a priori
expectations. As a result, many web sites are difficult to use and
difficult to design and maintain.
As a solution, we propose Adaptive Web Sites: web sites that
improve their structure and presentation by learning from interactions
with users. Examples include personalized recommendations, intelligent
"tour guides", and automatic customization. Our own system observes
the behavior of visitors to a site to find topics of interest and
creates new web pages on those topics. For example, the system might
notice that many visitors to an automobile information site view
information on various minivans, one at a time; the system would create
a new page with information on minivans to faciliate this comparison
shopping.
We have published a number of papers on this subject, including a
challenge paper in IJCAI '97[3]
as well as an article[5] for the AI Journal.
This problem domain has led us to new algorithms for statistical cluster
mining and conceptual cluster mining, as well as useful applications for
web sites. Some links and downloadable data are available at
UW CSE.
References.
[1]
M. Perkowitz and O. Etzioni
"Category Translation: Learning to
Understand Information on the Internet."
Proceedings of IJCAI95. pp. 930-936.
[2]
M. Perkowitz, R. Doorenbos, O. Etzioni, and D. Weld.
"Learning to Understand Information
on the Internet: an Example-Based Approach."
Journal of Intelligent Information Systems.
Vol. 8, num. 2, 1997. pp. 133-153.
[3]
M. Perkowitz and O. Etzioni
"Adaptive Web Sites: an AI Challenge."
Proceedings of IJCAI97.
[4]
M. Perkowitz, and O. Etzioni.
"Adaptive Web Sites:
Concept and Case Study."
Communications of the ACM.
To appear.
[5]
M. Perkowitz, and O. Etzioni.
"Towards Adaptive Web Sites:
Conceptual Framework and Case Study."
Artificial Intelligence Journal.
To appear.
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