[ please distribute - apologies for multiple postings ] ======================================== 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 ======================================== http://www.mathematik.uni-marburg.de/~kebi/ws-ecml-08/ taking place on September 19, 2008, as part of ECML/PKDD-08, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases September 15-19, 2008, Antwerp (Belgium) http://www.ecmlpkdd2008.org/ 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 information. This workshop 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 systems; # 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). ======================================== SUBMISSION INSTRUCTIONS ======================================== Papers MUST be submitted in Springer LNCS format. 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: http://www.springer.de/comp/lncs/authors.html Submit papers in PDF or PS format. Additional instructions will be given in due time. Papers should be submitted in pdf format to the following email address: [EMAIL PROTECTED] ======================================== IMPORTANT DATES ======================================== JUN 23 Deadline for workshop paper submission JUL 31 Notification of acceptance for workshop papers AUG 18 Final camera ready copies due ======================================== WORKSHOP CHAIRS ======================================== Eyke Huellermeier Department of Mathematics and Computer Science University of Marburg, Germany [EMAIL PROTECTED] Johannes Fuernkranz Department of Computer Science Technical University of Darmstadt, Germany [EMAIL PROTECTED] ======================================== WORKSHOP-WEBSITE ======================================== For further information, please visit the workshop website at http://www.mathematik.uni-marburg.de/~kebi/ws-ecml-08/ or contact one of the workshop co-chairs. Eyke Huellermeier and Johannes Fuernkranz Workshop Chairs
