Preference Learning

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 ́ђأ 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 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 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.