Fürnkranz J., Hüllermeier E. (Eds.) Preference Learning

Springer, 2010. — 474 p. — ISBN: 978-3642141249, e-ISBN: 978-3642141256.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 previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. 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. And, generalizing beyond training data, models thus learned may be used for preference prediction.
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 first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems.
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.Preference Learning: An Introduction.
A Preference Optimization Based Unifying Framework for Supervised Learning Problems.
Part I. Label Ranking.
Label Ranking Algorithms: A Survey.
Preference Learning and Ranking by Pairwise Comparison.
Decision Tree Modeling for Ranking Data.
Co-Regularized Least-Squares for Label Ranking.
Part II. Instance Ranking.
A Survey on ROC-Based Ordinal Regression.
Ranking Cases with Classification Rules.
Part III. Object Ranking.
A Survey and Empirical Comparison of Object Ranking Methods.
Dimension Reduction for Object Ranking.
Learning of Rule Ensembles for Multiple Attribute Ranking Problems.
Part IV. Preferences in Multi-Attribute Domains.
Learning Lexicographic Preference Models.
Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets.
Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models.
Learning Aggregation Operators for PreferenceModeling.
Part V. Preferences in Information Retrieval.
Evaluating Search Engine Relevance with Click-Based Metrics.
Learning SVM Ranking Functions from User Feedback.
Using Document Metadata and Active Learning in the Biomedical Domain.
Part VI. Preferences in Recommender Systems.
Learning Preference Models in Recommender Systems.
Collaborative Preference Learning.
Discerning Relevant Model Features in a Content-Based Collaborative Recommender System.