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Preference Learning: An Introduction

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Preference Learning

Abstract

This introduction gives a brief overview of the field of preference learning and, along the way, tries to establish a unified terminology. Special emphasis will be put on learning to rank, which is by now one of the most extensively studied problem tasks in preference learning and also prominently represented in this book. We propose a categorization of ranking problems into object ranking, instance ranking, and label ranking. Moreover, we introduce these scenarios in a formal way, discuss different ways in which the learning of ranking functions can be approached, and explain how the contributions collected in this book relate to this categorization. Finally, we also highlight some important applications of preference learning methods.

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Notes

  1. 1.

    Besides, one should be aware of conflicts between terminology in different fields. In the field of operations research, for example, the term “ranking” is used for arranging a set of objects in a total order, while “sorting” refers to the assignment of objects to an ordered set of categories, a problem closely related to what is called “ordered classification” in machine learning.

  2. 2.

    In a sense, this alternative is not just a formally equivalent rewriting. Instead, by considering an instance/label pair as an object, it suggests a natural way to unify the problems of object and label ranking.

  3. 3.

    More general approximations can be realized by labeling a leaf node with a nonconstant function, for example a linear function in regression learning.

  4. 4.

    http://research.microsoft.com/en-us/um/beijing/projects/letor/

References

  1. R. Balasubramaniyan, E. Hüllermeier, N. Weskamp, J. Kämper, Clustering of gene expression data using a local shape-based similarity measure. Bioinformatics 21(7), 1069–1077 (2005)

    Article  Google Scholar 

  2. A. Birlutiu, T. Heskes, Expectation propagation for rating players in sports competitions, in Proceedings of the 11th European Symposium on Principles of Knowledge Discovery in Databases (PKDD-07), ed. by J.N. Kok, J. Koronacki, R. López de Mántaras, S. Matwin, D. Mladenić, A. Skowron (Springer, Warsaw, Poland, 2007), pp. 374–381

    Google Scholar 

  3. C. Boutilier, R. Brafman, C. Domshlak, H. Hoos, D. Poole, CP-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Intell. Res. 21, 135–191 (2004)

    MathSciNet  MATH  Google Scholar 

  4. D. Bouyssou, Ranking methods based on valued preference relations: A characterization of the net flow method. Eur. J. Oper. Res. 60(1), 61–67 (1992)

    Article  MATH  Google Scholar 

  5. A.P. Bradley, The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7), 1145–1159 (1997)

    Article  Google Scholar 

  6. R.I. Brafman, Preferences, planning and control, in Proceedings of the 11th Conference on Principles of Knowledge Representation and Reasoning (KR-08), ed. by G. Brewka, J. Lang (AAAI, Sydney, Australia, 2008), pp. 2–5

    Google Scholar 

  7. P.B. Brazdil, C. Soares, J.P. da Costa, Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results. Mach. Learn. 50(3), 251–277 (2003)

    Article  MATH  Google Scholar 

  8. J.S. Breese, D. Heckerman, C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, in Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), ed. by G.F. Cooper, S. Moral (Morgan Kaufmann, Madison, WI, 1998), pp. 43–52

    Google Scholar 

  9. K. Brinker, E. Hüllermeier, Case-based label ranking, in Proceedings of the 17th European Conference on Machine Learning (ECML-06), ed. by J. Fürnkranz, T. Scheffer, M. Spiliopoulou (Springer, Berlin, Germany, 2006), pp. 566–573

    Google Scholar 

  10. K. Brinker, E. Hüllermeier, Case-based multilabel ranking, in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07), ed. by M.M. Veloso (Hyderabad, India, 2007), pp. 702–707

    Google Scholar 

  11. W. Cheng, J. Hühn, E. Hüllermeier, Decision tree and instance-based learning for label ranking, in Proceedings of the 26th International Conference on Machine Learning (ICML-09), ed. by A. Danyluk, L. Bottou, M.L. Littman (Montreal, Canada, 2009)

    Google Scholar 

  12. W.W. Cohen, R.E. Schapire, Y. Singer, Learning to order things. J. Artif. Intell. Res. 10, 243–270 (1999)

    MathSciNet  MATH  Google Scholar 

  13. D. Coppersmith, L. Fleischer, A. Rudra, Ordering by weighted number of wins gives a good ranking for weighted tournaments, in Proceedings of the 17th ACM-SIAM Symposium on Discrete Algorithms (SODA-06) (2006), pp. 776–782

    Google Scholar 

  14. O. Dekel, C.D. Manning, Y. Singer, Log-linear models for label ranking, in Advances in Neural Information Processing Systems (NIPS-03), ed. by S. Thrun, L.K. Saul, B. Schölkopf (MIT, Cambridge, MA, 2004), pp. 497–504

    Google Scholar 

  15. J. Doyle, Prospects for preferences. Comput. Intell. 20(2), 111–136 (2004)

    Article  MathSciNet  Google Scholar 

  16. C. Dwork, R. Kumara, M. Naor, D. Sivakumar, Rank aggregation methods for the Web, in Proceedings of the 10th International World Wide Web Conference (WWW-01) (ACM, Hong Kong, China, 2001), pp. 613–622

    Google Scholar 

  17. R. Fagin, R. Kumar, D. Sivakumar, Comparing top k lists. SIAM J. Discrete Math. 17(1), 134–160 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  18. J. Fürnkranz, E. Hüllermeier, Pairwise preference learning and ranking, in Proceedings of the 14th European Conference on Machine Learning (ECML-03), vol. 2837, Lecture Notes in Artificial Intelligence, ed. by N. Lavrač, D. Gamberger, H. Blockeel, L. Todorovski (Springer, Cavtat, Croatia, 2003), pp. 145–156

    Google Scholar 

  19. J. Fürnkranz, E. Hüllermeier, S. Vanderlooy, Binary decomposition methods for multipartite ranking, in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD-09), vol. Part I, ed. by Wray L. Buntine, M. Grobelnik, D. Mladenic, J. Shawe-Taylor (Springer, Bled, Slovenia, 2009), pp. 359–374

    Google Scholar 

  20. X. Geng, T.-Y. Liu, T. Qin, A. Arnold, H. Li, H.-Y. Shum, Query dependent ranking using k-nearest neighbor, In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-08), ed. by S.-H. Myaeng, D.W. Oard, F. Sebastiani, T.-S. Chua, M.-K. Leong (ACM, Singapore, 2008), pp. 115–122

    Chapter  Google Scholar 

  21. D. Goldberg, D. Nichols, B.M. Oki, D. Terry, Using collaborative filtering to weave and information tapestry. Commun. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  22. M. Gönen, G. Heller, Concordance probability and discriminatory power in proportional hazards regression. Biometrika 92(4), 965–970 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  23. S. Gordon, M. Truchon, Social choice, optimal inference and figure skating. Soc. Choice Welfare 30(2), 265–284 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  24. P. Haddawy, V. Ha, A. Restificar, B. Geisler, J. Miyamoto, Preference elicitation via theory refinement. J. Mach. Learn. Res. 4, 317–337 (2003)

    MathSciNet  Google Scholar 

  25. S. Har-Peled, D. Roth, D. Zimak, Constraint classification: A new approach to multiclass classification, in Proceedings of the 13th International Conference on Algorithmic Learning Theory (ALT-02), ed. by N. Cesa-Bianchi, M. Numao, R. Reischuk (Springer, Lübeck, Germany, 2002), pp. 365–379

    Chapter  Google Scholar 

  26. S. Har-Peled, D. Roth, D. Zimak, Constraint classification for multiclass classification and ranking, in Advances in Neural Information Processing Systems 15 (NIPS-02), ed. by S. Becker, S. Thrun, K. Obermayer (2003), pp. 785–792

    Google Scholar 

  27. R. Herbrich, T. Graepel, K. Obermayer, Large margin rank boundaries for ordinal regression, in Advances in Large Margin Classifiers, ed. by P.J. Bartlett, B. Schölkopf, D. Schuurmans, A.J. Smola (MIT, 2000), pp. 115–132

    Google Scholar 

  28. E. Hüllermeier, J. Fürnkranz, Learning label preferences: Ranking error versus position error, in Advances in Intelligent Data Analysis: Proceedings of the 6th International Symposium (IDA-05) (Springer, Madrid, Spain, 2005), pp. 180–191

    Google Scholar 

  29. E. Hüllermeier, J. Fürnkranz (eds.), Proceedings of the ECML/PKDD-08 Workshop on Preference Learning (Antwerp, Belgium, 2008)

    Google Scholar 

  30. E. Hüllermeier, J. Fürnkranz (eds.), Proceedings of the ECML/PKDD-09 Workshop on Preference Learning (Bled, Slovenia, 2009)

    Google Scholar 

  31. E. Hüllermeier, J. Fürnkranz, W. Cheng, K. Brinker, Label ranking by learning pairwise preferences. Artif. Intell. 172, 1897–1916 (2008)

    Article  MATH  Google Scholar 

  32. K. Järvelin, J. Kekäläinen, Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)

    Article  Google Scholar 

  33. T. Joachims, Optimizing search engines using clickthrough data, in Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-02) (ACM, 2002), pp. 133–142

    Google Scholar 

  34. T. Joachims, L. Granka, B. Pan, H. Hembrooke, G. Gay, Accurately interpreting clickthrough data as implicit feedback, in Proceedings of the 28th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR-05) (2005), pp. 154–161

    Google Scholar 

  35. G. Lebanon, J. Lafferty, Cranking: Combining rankings using conditional probability models on permutations, in Proceedings of the 19th International Conference on Machine Learning (ICML-02), ed. by C. Sammut, A. Hoffmann (Sydney, Australia, 2002), pp. 363–370

    Google Scholar 

  36. T.-Y. Lu, Learning to rank for information retrieval. Found. Trends Inf. Retrieval 3(3), 225–331 (2009)

    Article  Google Scholar 

  37. R. Duncan Luce, H. Raiffa, Games and Decisions: Introduction and Critical Survey (Wiley, New York, NY, 1957)

    MATH  Google Scholar 

  38. H.B. Mann, D.R. Whitney, On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(50), 50–60 (1947)

    Google Scholar 

  39. C.D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval (Cambridge University Press, 2008)

    Google Scholar 

  40. W. Ni, Y. Huang, M. Xie, A query dependent approach to learning to rank for information retrieval, in Proceedings of the 9thh International Conference on Web-Age Information Management (WAIM-08) (IEEE, Zhangjiajie, China, 2008), pp. 262-269

    Google Scholar 

  41. F. Radlinski, T. Joachims, Learning to rank from implicit feedback, in Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD-05) (2005), pp. 239-248

    Google Scholar 

  42. S. Rajaram, S. Agarwal, Generalization bounds for k-partite ranking, in Proceedings of the NIPS 2005 Workshop on Learning to Rank, ed. by S. Agarwal, C. Cortes, R. Herbrich, (Whistler, BC, Canada, 2005), pp. 28–23

    Google Scholar 

  43. P. Resnick, H.R. Varian, Special issue on recommender systems. Commun. ACM 40(3), (1997)

    Google Scholar 

  44. G. Tesauro, Connectionist learning of expert preferences by comparison training. in Advances in Neural Information Processing Systems 1 (NIPS-88), ed. by D. Touretzky (Morgan Kaufmann, 1989), pp. 99–106

    Google Scholar 

  45. A. Ukkonen, K. Puolamäki, A. Gionis, H. Mannila, A randomized approximation algorithm for computing bucket orders. Inf. Process. Lett. 109, 356–359 (2009)

    Article  MATH  Google Scholar 

  46. J. Wang, Artificial neural networks versus natural neural networks: A connectionist paradigm for preference assessment. Decision Support Syst. 11, 415–429 (1994)

    Article  Google Scholar 

  47. F. Wilcoxon, Individual comparisons by ranking methods. Biometrics Bull. 1, 80–83 (1945)

    Article  Google Scholar 

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Acknowledgements

This research has been supported by the German Science Foundation (DFG).

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Authors and Affiliations

  1. Technical University Darmstadt, Darmstadt, Germany

    Johannes Fürnkranz

  2. Philipps-Universität Marburg, Marburg, Germany

    Eyke Hüllermeier

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  1. Johannes Fürnkranz
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Correspondence to Johannes Fürnkranz .

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  1. FB Informatik, TU Darmstadt, Hochschulstr. 10, Darmstadt, 64289, Germany

    Johannes Fürnkranz

  2. FB Mathematik und Informatik, Philipps-Universität Marburg, Hans-Meerwein-Str., Marburg, 35032, Germany

    Eyke Hüllermeier

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Fürnkranz, J., Hüllermeier, E. (2010). Preference Learning: An Introduction. In: Fürnkranz, J., Hüllermeier, E. (eds) Preference Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14125-6_1

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  • DOI: https://doi.org/10.1007/978-3-642-14125-6_1

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