Preference Learning: An Introduction
Inhaltsverzeichnis
Reference
Johannes Fürnkranz, Eyke Hüllermeier: Preference Learning: An Introduction. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 1-17.
DOI
http://dx.doi.org/10.1007/978-3-642-14125-6_1
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.
Extended Abstract
Bibtex
@incollection{
year={2011},
isbn={978-3-642-14124-9},
booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
doi={10.1007/978-3-642-14125-6_1},
title={Preference Learning: An Introduction},
url={http://dx.doi.org/10.1007/978-3-642-14125-6_1, http://de.evo-art.org/index.php?title=Preference_Learning:_An_Introduction },
publisher={Springer Berlin Heidelberg},
author={Fürnkranz, Johannes and Hüllermeier, Eyke},
pages={1-17},
language={English}
}
Used 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) http://dx.doi.org/10.1093/bioinformatics/bti095
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
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)http://www.ams.org/mathscinet-getitem?mr=2130726
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) http://dx.doi.org/10.1016/0377-2217(92)90333-5
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) http://dx.doi.org/10.1016/S0031-3203(96)00142-2
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
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) http://dx.doi.org/10.1023/A%3A1021713901879
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
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
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
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)
12. W.W. Cohen, R.E. Schapire, Y. Singer, Learning to order things. J. Artif. Intell. Res. 10, 243–270 (1999)
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
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
15. J. Doyle, Prospects for preferences. Comput. Intell. 20(2), 111–136 (2004) http://dx.doi.org/10.1111/j.0824-7935.2004.00233.x
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
17. R. Fagin, R. Kumar, D. Sivakumar, Comparing top k lists. SIAM J. Discrete Math. 17(1), 134–160 (2003) http://dx.doi.org/10.1137/S0895480102412856
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
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
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 http://dx.doi.org/10.1145/138859.138867
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) http://dx.doi.org/10.1145/138859.138867
22. M. Gönen, G. Heller, Concordance probability and discriminatory power in proportional hazards regression. Biometrika 92(4), 965–970 (2005) http://dx.doi.org/10.1093/biomet/92.4.965
23. S. Gordon, M. Truchon, Social choice, optimal inference and figure skating. Soc. Choice Welfare 30(2), 265–284 (2008) http://dx.doi.org/10.1007/s00355-007-0243-2
24. P. Haddawy, V. Ha, A. Restificar, B. Geisler, J. Miyamoto, Preference elicitation via theory refinement. J. Mach. Learn. Res. 4, 317–337 (2003)
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 http://dx.doi.org/10.1007/3-540-36169-3_29
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
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
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
29. E. Hüllermeier, J. Fürnkranz (eds.), Proceedings of the ECML/PKDD-08 Workshop on Preference Learning (Antwerp, Belgium, 2008)
30. E. Hüllermeier, J. Fürnkranz (eds.), Proceedings of the ECML/PKDD-09 Workshop on Preference Learning (Bled, Slovenia, 2009)
31. E. Hüllermeier, J. Fürnkranz, W. Cheng, K. Brinker, Label ranking by learning pairwise preferences. Artif. Intell. 172, 1897–1916 (2008) http://dx.doi.org/10.1016/j.artint.2008.08.002
32. K. Järvelin, J. Kekäläinen, Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002) http://dx.doi.org/10.1145/582415.582418
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
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
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
36. T.-Y. Lu, Learning to rank for information retrieval. Found. Trends Inf. Retrieval 3(3), 225–331 (2009) http://dx.doi.org/10.1561/1500000016
37. R. Duncan Luce, H. Raiffa, Games and Decisions: Introduction and Critical Survey (Wiley, New York, NY, 1957)
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)
39. C.D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval (Cambridge University Press, 2008)
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
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
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
43. P. Resnick, H.R. Varian, Special issue on recommender systems. Commun. ACM 40(3), (1997)
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
45. A. Ukkonen, K. Puolamäki, A. Gionis, H. Mannila, A randomized approximation algorithm for computing bucket orders. Inf. Process. Lett. 109, 356–359 (2009) http://dx.doi.org/10.1016/j.ipl.2008.12.003
46. J. Wang, Artificial neural networks versus natural neural networks: A connectionist paradigm for preference assessment. Decision Support Syst. 11, 415–429 (1994) http://dx.doi.org/10.1016/0167-9236(94)90016-7
47. F. Wilcoxon, Individual comparisons by ranking methods. Biometrics Bull. 1, 80–83 (1945) http://dx.doi.org/10.2307/3001968
Links
Full Text
http://www.ke.tu-darmstadt.de/publications/papers/PLBook-Introduction.pdf
Sonstige Links
http://link.springer.com/chapter/10.1007/978-3-642-14125-6_1
