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Thank you.
=========================================================================
Call for participation
Machine Learning for User Modeling
UM-2001 Workshop
(http://www.dfki.de/um2001/)
=========================================================================
User model acquisition is a difficult problem. The information
available
to a user modeling system is usually limited, and it is hard to infer
assumptions about the user that are strong enough to justify non-trivial
conclusions. Classical acquisition methods like user interviews,
application-specific heuristics, and stereotypical inferences often are
inflexible and unsatisfying.
Machine Learning is concerned with the formation of models from
observations. Hence, learning algorithms are promising candidates for
user
model acquisition. Additionally, the theory revision techniques
provided
by machine learning approaches may prove helpful in user model
maintenance.
In recent years, there has been a growing number of applications of
machine learning techniques to user-adapted interactions. While early
work was mainly done in the area of intelligent user interfaces,
machine learning methods have also become popular within the user
modeling community.
At UM97, a first workshop on "Machine Learning for User Modeling"
(ML4UM) took place, and a special interest group was initiated. The
second ML4UM workshop was held at the UM99. The ML4UM SIG now has
both a web site and a mailing list with about 150 subscribers. The
growing interest in machine learning techniques for user modeling and
adaptive systems is also reflected by the upcoming special issue on
Adaptive User Interfaces of the "Machine Learning" journal (see
http://www.isle.org/~aui/mljcfp.html).
The goal of the workshop is twofold: On the one hand, it attempts to
be
a forum for user modeling researchers who want to discuss specific
problems of using machine learning for user modeling. Both experts and
novices (and all those in between) are invited. On the other hand, the
workshop shall function as a SIG meeting, where joint activities of
interested attendants can be planned. Hence, there are two groups of
questions to be discussed at the workshop:
Research issues:
What learning tasks can be identified in user modeling systems?
Are there classes of problems in user modeling that are particularly
well or poorly suited to the application of machine learning methods?
Are there machine learning algorithms or classes of algorithms that are
particularly appropriate / not appropriate for user modeling systems?
Are there subareas of user modeling or classes of user modeling systems
where machine learning can be especially useful?
In what respects does the induction of a user model differ from other
induction tasks to which machine learning is typically applied, and what
implications does this have for the application of machine learning in
user modeling?
In the case of the description of a concrete application: Why did you
choose this particular machine learning technique? How did it affect
the
success of your application? What general conclusions can you draw from
your experiences?
Where / How does the user fit into the learning; what kind of
user feedback is helpful / needed, and how can the user query / use
the learned model?
SIG issues:
- - What has been done since the last SIG meeting ?
- - How can SIG facilities be made more useful?
- - What are possibilities for cooperation between SIG members?
- - What could be activities the SIG should engage in?
- - others
Participation and Paper Submission
==================================
Participants are required to submit a short paper that
- - describes why they are interested in the application of machine
learning techniques to user modeling and the problems and
questions they have encountered and/or
- - makes proposals concerning SIG activities and/or
- - describe their current work and interests as related to the workshop
topic
In the first two cases, authors shall provide comments and answers to
the questions above as topics of interest, and perhaps raise new
relevant questions and issues in about 2 pages. In the third case,
the work and interests should be described in no more than 10 pages.
Participants will be selected based on their submissions.
Organization
============
The workshop program will be content-centered. Related issues will be
grouped together into sessions, each of which will be moderated by one
other participant. Participants will be given opportunity to briefly
present their contributions, but they may be part of several sessions,
if
their paper covers several issues that are quite different from each
other.
In particular, research issues will be separated from SIG issues.
Accepted contributions will be distributed electronically to all
participants beforehand. A mailing list will be set up which
participants
will be encouraged to use for a-priori comments on other participants'
contributions.
Submission instructions
=======================
Please submit a short paper in PostScript, PDF, or HTML to
[EMAIL PROTECTED]
The final version should not exceed 10 pages.
There are no further formatting instructions for the first submission.
Though, we recommend to use the Springer LLNCS package.
Deadlines
=========
March 8 deadline for submissions
April 1 notification of authors about acceptance
April 27 deadline for revised versions of accepted contributions
May 11 accepted contributions and first draft of the workshop
program made available to participants;
mailing list for participants set up
Program Committee
=================
Ralph Schaefer, DFKI ,Germany, [EMAIL PROTECTED] (Organizer)
Martin E. Mueller, University of Osnabrueck, Germany,
[EMAIL PROTECTED] (Organizer)
Sofus Attila Macskassy, Rutgers University, U.S.A.,
[EMAIL PROTECTED] (Organizer)
Mathias Bauer, DFKI, Germany, [EMAIL PROTECTED]
Piotr Gmytrasiewicz, Univ. of Texas at Arlington, U.S.A.,
[EMAIL PROTECTED]
Mehmet Goeker, DaimlerChrysler Research and Technology, Palo Alto,
U.S.A.,
[EMAIL PROTECTED]
Ingo Schwab, GMD, St. Augustin, Germany, [EMAIL PROTECTED]
Jude Shavlik, University of Wisconsin, Madison, U.S.A.,
[EMAIL PROTECTED]
Frank Wittig, University of Saarbruecken, Germany, [EMAIL PROTECTED]
