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 .

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)