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CfP MLJ SI Preference Learning and Ranking

From: Johannes Fuernkranz <juffi@ke.informatik.tu-darmstadt.de>
Date: Fri, 14 Oct 2011 12:05:59 +0200
To: <www-rdf-logic@w3.org>
Message-Id: <E1REeeQ-0008MX-UO@kellerassel>
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Call for Papers

PREFERENCE LEARNING AND RANKING

Special Issue in Machine Learning
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BACKGROUND

Methods for learning and predicting preference models from explicit or
implicit preference information and feedback are among the very recent
research trends in machine learning and knowledge discovery. Approaches
relevant to this area range from learning special types of preference
models such as lexicographic orders over collaborative filtering
techniques for recommender systems and ranking techniques for
information retrieval, to generalizations of classification problems
such as label ranking. Like many complex learning tasks that have
recently entered the stage in the field of machine learning, preference
learning deviates strongly from the standard machine learning problems
of classification and regression. It is particularly challenging as it
involves the prediction of complex structures, such as weak or partial
order relations, rather than single values. Moreover, training input
will not, as it is usually the case, be offered in the form of complete
examples but may comprise more general types of information, such as
relative preferences or different kinds of indirect feedback. Authors
are invited to submit full papers presenting original results on any
aspect of machine learning and games. An ideal contribution to this
special issue would be strongly motivated by applications to commercial
or classical games and focused on research issues relevant to the topics
described below. Papers specific to game theory should not be submitted
to this special issue (there will be forthcoming special issue on this
topic).

SCOPE

Topics of interest to the special issue include, but are not limited to

  * quantitative and qualitative approaches to modeling preferences and
    different forms of feedback and training data;
  * learning utility functions and related regression problems;
  * preference mining, preference elicitation, and active learning;
  * learning relational preference models;
  * generalizations or special forms of classification problems, such as
    label ranking, ordinal classification, and hierarchical classification;
  * comparison of different preference learning paradigms (e.g.,
    learning of single models vs. modular approaches that decompose the
    problem into subproblems);
  * ranking problems, such as learning to rank objects or to aggregate
    rankings;
  * methods for special application fields, such as web search,
    information retrieval, electronic commerce, games, personalization,
    or recommender systems.
 

SUBMISSIONS

Titles and Short Abstracts: 	/December 31, 2011/
Submission Deadline: 	        /January 10, 2012/

If you intend to submit a paper to the special issue, please send a
short abstract per E-mail to both editors before December 31, 2011.

Submissions to the special issue must be submitted like regular
submissions to the journal. Instructions can be found at
<http://www.springer.com/computer/ai/journal/10994>.

Each submission will be reviewed according to the standards of the
Machine Learning Journal. All inquiries regarding this special issue
should also be directed to the guest editors.

We aim for a publication of the special issue in late 2012/early 2013.


GUEST EDITORS

Eyke Hüllermeier   (Philipps-Universität Marburg)
Johannes Fürnkranz (TU Darmstadt)
Received on Friday, 14 October 2011 10:06:41 GMT

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