© 2011

Preference Learning

  • Johannes Fürnkranz
  • Eyke Hüllermeier
  • This is the first book dedicated to this topic

  • This topic has attracted considerable attention in artificial intelligence research in recent years

  • A comprehensive treatment


Table of contents

  1. Front Matter
    Pages i-ix
  2. Label Ranking

    1. Front Matter
      Pages 43-43
    2. Johannes Fürnkranz, Eyke Hüllermeier
      Pages 1-17
  3. Label Ranking

    1. Front Matter
      Pages 43-43
    2. Shankar Vembu, Thomas Gärtner
      Pages 45-64
    3. Johannes Fürnkranz, Eyke Hüllermeier
      Pages 65-82
    4. Philip L. H. Yu, Wai Ming Wan, Paul H. Lee
      Pages 83-106
    5. Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Tom Heskes
      Pages 107-123
  4. Instance Ranking

    1. Front Matter
      Pages 125-125
    2. Willem Waegeman, Bernard De Baets
      Pages 127-154
    3. Jianping Zhang, Jerzy W. Bala, Ali Hadjarian, Brent Han
      Pages 155-177
  5. Object Ranking

    1. Front Matter
      Pages 179-179
    2. Toshihiro Kamishima, Hideto Kazawa, Shotaro Akaho
      Pages 181-201
    3. Toshihiro Kamishima, Shotaro Akaho
      Pages 203-215
    4. Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński, Marcin Szeląg
      Pages 217-247
  6. Preferences in Multi-Attribute Domains

    1. Front Matter
      Pages 249-249
    2. Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins
      Pages 251-272
    3. Yann Chevaleyre, Frédéric Koriche, Jérôme Lang, Jérôme Mengin, Bruno Zanuttini
      Pages 273-296
    4. Joachim Giesen, Klaus Mueller, Bilyana Taneva, Peter Zolliker
      Pages 297-315

About this book


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.


Artificial intelligence Data mining Information retrieval Instance ranking Label ranking Learning Machine learning Multicriteria decision-making Object ranking Operations research Preference learning Preference prediction Reasoning Recommender systems Supevised learning

Editors and affiliations

  • Johannes Fürnkranz
    • 1
  • Eyke Hüllermeier
    • 2
  1. 1.FB InformatikTU DarmstadtDarmstadtGermany
  2. 2.FB Mathematik und InformatikPhilipps-Universität MarburgMarburgGermany

Bibliographic information

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From the 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)