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AISB event Bulletin Item

2nd CFP: ECML/PKDD-09 Workshop on Preference Learning

2nd C A L L  F O R  P A P E R S

			 W O R K S H O P  O N

		P R E F E R E N C E   L E A R N I N G

taking place on September 11, 2009, as part of

ECML/PKDD-09, European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases

September 7-11, 2009, Bled (Slovenia)


Methods for learning preference models and predicting preferences are
among the very recent research trends in fields like 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
other types of complex learning tasks that have recently entered the
stage, preference learning deviates strongly from the standard
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 and implicit preference


This workshop is a follow-up activity of PL-08, the first workshop on
Preference Learning that has been organized successfully as part of
ECML/PKDD-2008 in Antwerp. It aims at providing a forum for the
discussion of recent advances in the use of machine learning and data
mining methods for problems related to the learning and discovery of
preferences, and to offer an opportunity for researchers and
practitioners to identify new promising research directions. Topics of
interest include, but are not limited to

* quantitative and qualitative approaches to modeling 
  preferences as well as different forms of feedback and training data;
* learning utility functions and related regression problems;
* preference mining and preference elicitation;
* learning relational preference models;
* embedding of other types of learning problems in the preference 
  learning framework (such as label ranking, ordinal classification, 
  or hierarchical classification);
* comparison of different preference learning paradigms (e.g., "big bang" 
  approaches that use a single model vs. modular approaches that decompose 
  the learning of preference models into subproblems);
* ranking problems, such as learning to rank objects or to aggregate rankings;
* scalability and efficiency of preference learning algorithms;
* methods for special application fields, such as web search, information
  retrieval, electronic commerce, games, personalization, or recommender 
* connections to other research fields, such as decision theory, operations
  research, and social choice theory.

In addition to papers reporting on mature research results we also
encourage submissions presenting more preliminary results and
discussing open problems. Correspondingly, two types of contributions
will be solicited, namely short communications (short talks) and full
papers (long talks).


Papers must be formatted in Springer LNCS style and submitted in PDF
to one of the organizers. There is no strict page limitation, though
10-15 pages for full papers and 6-8 pages for short communications
should be taken as rough guidelines. Authors' instructions along with
LaTeX and Word macro files are available on the web at:

Please send submissions to .

JUN 15     Deadline for workshop paper submission

Eyke Huellermeier
Department of Mathematics and Computer Science
University of Marburg, Germany

Johannes Fuernkranz
Department of Computer Science
Technical University of Darmstadt, Germany

For further information, please visit the workshop website at
or contact the workshop co-chairs at 

Eyke Huellermeier and Johannes Fuernkranz
Workshop Chairs