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 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.

[1]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[2]  Thomas Gärtner,et al.  Label Ranking Algorithms: A Survey , 2010, Preference Learning.

[3]  Eyke Hüllermeier,et al.  Preference Learning and Ranking by Pairwise Comparison , 2010, Preference Learning.

[4]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[5]  Eyke Hüllermeier,et al.  Preference Learning: An Introduction , 2010, Preference Learning.

[6]  Eyke Hüllermeier,et al.  Binary Decomposition Methods for Multipartite Ranking , 2009, ECML/PKDD.

[7]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[8]  Stephen Gordon,et al.  Social choice, optimal inference and figure skating , 2008, Soc. Choice Welf..

[9]  David A. Forsyth,et al.  Matching Words and Pictures , 2003, J. Mach. Learn. Res..

[10]  Tie-Yan Liu,et al.  Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.

[11]  Daphne Koller,et al.  Hierarchically Classifying Documents Using Very Few Words , 1997, ICML.

[12]  Koby Crammer,et al.  Pranking with Ranking , 2001, NIPS.

[13]  Eyke Hüllermeier,et al.  Pairwise Preference Learning and Ranking , 2003, ECML.

[14]  Ronald Fagin,et al.  Comparing top k lists , 2003, SODA '03.

[15]  Pasquale Lops,et al.  Learning Preference Models in Recommender Systems , 2010, Preference Learning.

[16]  Yann Chevaleyre,et al.  Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets , 2010, Preference Learning.

[17]  David A. Forsyth,et al.  Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary , 2002, ECCV.

[18]  D. Bouyssou.  Ranking methods based on valued preference relations: A characterization of the net flow method , 1992 .

[19]  Joydeep Ghosh,et al.  Scalable Clustering Algorithms with Balancing Constraints , 2006, Data Mining and Knowledge Discovery.

[20]  Jon Doyle,et al.  Prospects for Preferences , 2004, Comput. Intell..

[21]  Tom Heskes,et al.  Expectation Propagation for Rating Players in Sports Competitions , 2007, PKDD.

[22]  Thomas Hofmann,et al.  Support vector machine learning for interdependent and structured output spaces , 2004, ICML.

[23]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[24]  Filip Radlinski.  Learning to rank from implicit feedback , 2008 .

[25]  Eric Eaton,et al.  Learning user preferences for sets of objects , 2006, ICML.

[26]  François Pachet,et al.  Improving Multilabel Analysis of Music Titles: A Large-Scale Validation of the Correction Approach , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[27]  Filip Radlinski,et al.  Evaluating Search Engine Relevance with Click-Based Metrics , 2010, Preference Learning.

[28]  Ronen I. Brafman,et al.  CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements , 2011, J. Artif. Intell. Res..

[29]  D. Coppersmith,et al.  Ordering by weighted number of wins gives a good ranking for weighted tournaments , 2006, SODA 2006.

[30]  Eyke Hüllermeier,et al.  Label ranking by learning pairwise preferences , 2008, Artif. Intell..

[31]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[32]  Yoram Singer,et al.  Learning to Order Things , 1997, NIPS.

[33]  Ronen I. Brafman.  Preferences, Planning and Control , 2008, KR.

[34]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[35]  Eyke Hüllermeier,et al.  Decision tree and instance-based learning for label ranking , 2009, ICML '09.

[36]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[37]  Weijian Ni,et al.  A Query Dependent Approach to Learning to Rank for Information Retrieval , 2008, 2008 The Ninth International Conference on Web-Age Information Management.

[38]  Dan Roth,et al.  Constraint Classification: A New Approach to Multiclass Classification , 2002, ALT.

[39]  Marcel Worring,et al.  The challenge problem for automated detection of 101 semantic concepts in multimedia , 2006, MM '06.

[40]  Nir Ailon,et al.  Aggregating inconsistent information: Ranking and clustering , 2008 .

[41]  Gerald Tesauro,et al.  Connectionist Learning of Expert Preferences by Comparison Training , 1988, NIPS.

[42]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[43]  Eyke Hüllermeier,et al.  Clustering of gene expression data using a local shape-based similarity measure , 2005, Bioinform..

[44]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[45]  Grigorios Tsoumakas,et al.  Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..

[46]  Moni Naor,et al.  Rank aggregation methods for the Web , 2001, WWW '01.

[47]  Shotaro Akaho,et al.  A Survey and Empirical Comparison of Object Ranking Methods , 2010, Preference Learning.

[48]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[49]  Judy Goldsmith,et al.  Preference Handling for Artificial Intelligence , 2008, AI Mag..

[50]  John D. Lafferty,et al.  Cranking: Combining Rankings Using Conditional Probability Models on Permutations , 2002, ICML.

[51]  Carlos Soares,et al.  Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results , 2003, Machine Learning.

[52]  Johannes Fürnkranz,et al.  Round Robin Classification , 2002, J. Mach. Learn. Res..

[53]  Eyke Hüllermeier,et al.  Multilabel classification via calibrated label ranking , 2008, Machine Learning.

[54]  Eyke Hüllermeier,et al.  Preferences in AI: An overview , 2011, Artif. Intell..

[55]  Eyke Hüllermeier,et al.  On predictive accuracy and risk minimization in pairwise label ranking , 2010, J. Comput. Syst. Sci..

[56]  Charles Elkan,et al.  Predicting labels for dyadic data , 2010, Data Mining and Knowledge Discovery.

[57]  Yiming Yang,et al.  An Evaluation of Statistical Approaches to Text Categorization , 1999, Information Retrieval.

[58]  Noga Alon,et al.  Ranking Tournaments , 2006, SIAM J. Discret. Math..

[59]  M. Gonen,et al.  Concordance probability and discriminatory power in proportional hazards regression , 2005 .

[60]  John Langford,et al.  Robust reductions from ranking to classification , 2007, Machine Learning.

[61]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[62]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[63]  Peter Haddawy,et al.  Preference Elicitation via Theory Refinement , 2003, J. Mach. Learn. Res..

[64]  J. Hooker.  In This Volume , 2012 .

[65]  Dan Roth,et al.  Constraint Classification for Multiclass Classification and Ranking , 2002, NIPS.

[66]  Ralf Herbrich,et al.  Large margin rank boundaries for ordinal regression , 2000 .

[67]  Alessandro Sperduti,et al.  A Preference Optimization Based Unifying Framework for Supervised Learning Problems , 2010, Preference Learning.

[68]  Eyke Hüllermeier,et al.  Graded Multilabel Classification: The Ordinal Case , 2010, ICML.

[69]  Yoram Singer,et al.  Log-Linear Models for Label Ranking , 2003, NIPS.

[70]  H. Raiffa,et al.  GAMES AND DECISIONS; INTRODUCTION AND CRITICAL SURVEY. , 1958 .

[71]  S. Rajaram,et al.  Generalization Bounds for k-Partite Ranking , 2005 .