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Subject:
From:
Ingrid Zukerman <[log in to unmask]>
Reply To:
Ingrid Zukerman <[log in to unmask]>
Date:
Sun, 21 Aug 2005 22:47:39 +1000
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                    *** Apologies for multiple postings ***

              **** NOTE: Submission Deadline is Oct. 31, 2005 ****

                        SECOND CALL FOR PAPERS
                           Special Issue on
	   Statistical and Probabilistic Methods for User Modeling

                  User Modeling and User-Adapted Interaction:
                    The Journal of Personalization Research
            (an international journal published by Springer Verlag)

BACKGROUND:
The growth in popularity of on-line resources highlights the importance
of developing machinery to access these resources. At the same time,
the access to these resources by large numbers of users provides
opportunities for collecting and leveraging vast amounts of data
about user activity. In the last decade, there have been significant
developments in probabilistic, data-intensive technologies for accessing
information in on-line resources. The field of user modeling has also
been part of this trend, with the development of statistical models of
the behaviour, capabilities and preferences of users and groups of users.

This Special Issue of User Modeling and User-Adapted Interaction
will explore recent developments in different aspects of statistical
and probabilistic techniques for user modeling. Contributions are
particularly welcome in, but not limited to, the following areas:

-- user modeling applications of machine learning and statistical
   techniques, such as Bayesian networks, decision trees and graphs,
   clustering techniques, decision-theoretic approaches, and neural
   networks.

-- theoretical developments in statistical modeling and machine learning
   relevant to user modeling issues.

-- methods for the statistical evaluation of user models.

-- adaptations of user models over time (including cold start and
   concept drift).

-- combination of content-based and collaborative user models.

-- learning statistical user models from data sets with different
   characteristics, e.g., imbalanced data sets, very large data sets,
   and synthetic data sets.

HOW TO SUBMIT:
Submissions to the special issue should follow the UMUAI submission
instructions which are obtainable from the Web site
    http://www.umuai.org
Electronic submissions are preferred.  Each submission should note that
it is intended for the Special Issue on Statistical and Probabilistic
Methods for User Modeling. UMUAI is an archival journal that publishes
mature and substantiated research results on the (dynamic) adaptation
of computer systems to their human users, and the role that a model of
the system about the user plays in this context. Many articles in UMUAI
are quite comprehensive and describe the results of several years of
work. Consequently, UMUAI gives "unlimited" space to authors (so long
as what they write is important).

Potential authors are asked to notify the guest editors (David Albrecht
email: [log in to unmask], and Ingrid Zukerman, email:
[log in to unmask]) as soon as possible of their intent to
submit an article.  Sometime thereafter (but preferably a month prior
to the submission deadline), they should submit a tentative title and
short abstract (which can be altered for the actual submission) to
assist in the formation of a panel of appropriate reviewers.

REVIEW PROCESS:
Submissions will undergo the normal review process, and will be
reviewed by three established researchers selected from a panel of
reviewers formed for the special issue.  Barring unforeseen problems,
authors can expect to be notified regarding the review results within
three months of submission.

IMPORTANT DATES:
    Notification of Intent to Submit: as soon as possible
    Deadline Date for Submissions: October 31, 2005

Please address any questions to the guest editors:
David Albrecht and Ingrid Zukerman
Computer Science and Software Eng.
Monash University
http://www.csse.monash.edu.au/~{dwa,ingrid}
Phone: +61 3 9905-5{526,202}
Email: {dwa,[log in to unmask]


Alfred Kobsa
University of California, Irvine
http://www.ics.uci.edu/~kobsa

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