Preference Learning

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Reference

Johannes Fürnkranz, Eyke Hüllermeier (eds.): Preference Learning. Springer Berlin Heidelberg, 2011. ISBN: 978-3-642-14124-9 (Print) 978-3-642-14125-6 (Online)

DOI

http://dx.doi.org/10.1007/978-3-642-14125-6

Abstract

The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in recent years. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. Preference learning is concerned with the acquisition of preference models from data – it involves learning from observations that reveal information about the preferences of an individual or a class of individuals, and building models that generalize beyond such training data. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The remainder of the book is organized into parts that follow the developed framework, complementing survey articles with in-depth treatises of current research topics in this area. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.

Extended Abstract

Reviews

"The book looks at three major types of preference learning: label ranking, instance ranking, and object ranking. … chapters contain case studies and actual experiments to illustrate the claims made within. … this is a useful book in an emerging and important area, and hence would be of interest to machine learning researchers. The book is quite readable to that audience, despite a heavy emphasis on formal treatment." M. Sasikumar, ACM Computing Reviews, September, 2011


Bibtex

@book{
year={2011},
isbn={978-3-642-14124-9 (Print), 978-3-642-14125-6 (Online)},
booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
doi={10.1007/978-3-642-14125-6},
url={http://dx.doi.org/10.1007/978-3-642-14125-6, http://de.evo-art.org/index.php?title=Preference_Learning },
publisher={Springer Berlin Heidelberg},
language={English}
}

Table of contents (20 chapters)

Introduction


Label Ranking


Instance Ranking


Object Ranking


Preferences in Multi-Attribute Domains


Preferences in Information Retrieval


Preferences in Recommender Systems


Links

Full Text

intern file

Sonstige Links

Additional Infos from http://www.preference-learning.org

Workshops

  • Preference Learning (PL-08) at ECML/PKDD 2008
  • Special Session on Learning (with) Preferences at ESANN 2009


Tutorials

  • Preference Learning (J. Fürnkranz & E. Hüllermeier)
ECML/PKDD 2010 (Slides, Video) http://www.ke.tu-darmstadt.de/events/PL-10/#accepted http://www.ecmlpkdd2010.org/www.ecmlpkdd2010.org/tutorials/Web-Mining-PKDD.pdf http://videolectures.net/ecmlpkdd2010_hullermeier_furnkranz_pl/
DS 2011 (Slides) http://www.preference-learning.org/PL-Tutorial-DS-11.pdf
ECAI 2012 http://www.ke.tu-darmstadt.de/events/PL-12/tutorial.html
  • Learning to rank in vector spaces and social networks (Soumen Chakrabarti)
WWW 2007 http://www.cse.iitb.ac.in/~soumen/doc/www2007/
  • Learning to rank for information retrieval (Tie-Yan Liu)
WWW 2009 http://www2009.org/pdf/T7A-LEARNING%20TO%20RANK%20TUTORIAL.pdf
WWW 2008 http://research.microsoft.com/en-us/people/tyliu/learning_to_rank_tutorial_-_www_-_2008.pdf
SIGIR 2008 http://research.microsoft.com/en-us/people/tyliu/letor-tutorial-sigir08.pdf
  • Learning to rank (Yisong Yue, Filip Radlinski)
NESCAI 2008 http://www.cs.cornell.edu/Conferences/nescai/nescai08/tutorials.php#l2r
  • Learning to rank (Hang Li)
ACL-IJCNLP 2009 http://research.microsoft.com/en-us/people/hangli/li-acl-ijcnlp-2009-tutorial.pdf
ACML 2010 http://research.microsoft.com/en-us/people/hangli/acml-tutorial.pdf


Special Issues

  • Tie-Yan Liu, Thorsten Joachims, Hang Li and Chengxiang Zhai, Special Issue on Learning to Rank for Information Retrieval, Information Retrieval 13(3), 2010.


Books

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