Johannes Fürnkranz, Eyke Hüllermeier
Springer Science & Business Media, Nov 19, 2010 - Computers - 466 pages
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.
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Part I Label Ranking
Part II Instance Ranking
Part III Object Ranking
Part IV Preferences in MultiAttribute Domains
Other editions - View all
aggregation approach approximation Artificial Intelligence attributes binary choice experiment classification clicks collaborative filtering complexity computed Conference on Machine conjoint analysis consider consistent constraints CP-net Data Mining data set decision rules decision tree defined denote documents estimate evaluation examples feedback Fürnkranz given graph Hüllermeier information retrieval input instance Interleaving International Conference kernel label ranking learner learning algorithm linear loss function Mach Machine Learning matrix methods minimizing multiclass multilabel node object ranking observations optimization problem ordinal regression ORIG parameters partial order performance prediction preference learning preference model preference relation Proceedings proposed queries ranking function ranking problem RankRLS ratings RCDR recommender systems relevant ROC curve rough set sample orders score search engine Sect subset supervised learning support vector support vector machines SVOR target techniques threshold total order user profile utility function values variables voting weighted