Preference Learning [recurso electrónico] / edited by Johannes Fürnkranz, Eyke Hüllermeier.

Preference Learning: An Introduction -- A Preference Optimization Based Unifying Framework for Supervised Learning Problems -- 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 -- A Survey on ROC-Based Ordinal Regression -- Ranking Cases with Classification Rules -- A Survey and Empirical Comparison of Object Ranking Methods -- Dimension Reduction for Object Ranking -- Learning of Rule Ensembles for Multiple Attribute Ranking Problems -- 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 Preference Modeling -- Evaluating Search Engine Relevance with Click-Based Metrics -- Learning SVM Ranking Function from User Feedback Using Document -- Metadata and Active Learning in the Biomedical Domain -- Learning Preference Models in Recommender Systems -- Collaborative Preference Learning -- Discerning Relevant Model Features in a Content-Based Collaborative Recommender System -- Author Index -- Subject Index.

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.

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