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.
Author(s): Johannes Fürnkranz, Eyke Hüllermeier (auth.), Johannes Fürnkranz, Eyke Hüllermeier (eds.)
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2011
Language: English
Pages: 466
Tags: Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery
Front Matter....Pages i-ix
Front Matter....Pages 43-43
Preference Learning: An Introduction....Pages 1-17
A Preference Optimization Based Unifying Framework for Supervised Learning Problems....Pages 19-42
Front Matter....Pages 43-43
Label Ranking Algorithms: A Survey....Pages 45-64
Preference Learning and Ranking by Pairwise Comparison....Pages 65-82
Decision Tree Modeling for Ranking Data....Pages 83-106
Co-Regularized Least-Squares for Label Ranking....Pages 107-123
Front Matter....Pages 125-125
A Survey on ROC-based Ordinal Regression....Pages 127-154
Ranking Cases with Classification Rules....Pages 155-177
Front Matter....Pages 179-179
A Survey and Empirical Comparison of Object Ranking Methods....Pages 181-201
Dimension Reduction for Object Ranking....Pages 203-215
Learning of Rule Ensembles for Multiple Attribute Ranking Problems....Pages 217-247
Front Matter....Pages 249-249
Learning Lexicographic Preference Models....Pages 251-272
Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets....Pages 273-296
Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models....Pages 297-315
Learning Aggregation Operators for Preference Modeling....Pages 317-333
Front Matter....Pages 335-335
Evaluating Search Engine Relevance with Click-Based Metrics....Pages 337-361
Learning SVM Ranking Functions from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain....Pages 363-383
Front Matter....Pages 385-385
Learning Preference Models in Recommender Systems....Pages 387-407
Collaborative Preference Learning....Pages 409-427
Discerning Relevant Model Features in a Content-based Collaborative Recommender System....Pages 429-455
Back Matter....Pages 457-466
