Advertisement

A Survey and Empirical Comparison of Object Ranking Methods

  • Toshihiro Kamishima
  • Hideto Kazawa
  • Shotaro Akaho
Chapter

Abstract

Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results or bestseller lists. In spite of their importance, methods of processing orders have received little attention. However, research concerning orders has recently become common; in particular, researchers have developed various methods for the task of Object Ranking to acquire functions for object sorting from example orders. Here, we give a unified view of these methods and compare their merits and demerits.

Keywords

Ranking Function Weak Learner Ranking Feature Object Pair Sample Order 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

A part of this work is supported by the grant-in-aid 14658106 and 16700157 of the Japan society for the promotion of science. Thanks are due to the Mainichi Newspapers for permission to use the articles.

References

  1. 1.
    J.I. Marden, Analyzing and Modeling Rank Data, Vol. 64, Monographs on Statistics and Applied Probability (Chapman & Hall, 1995)Google Scholar
  2. 2.
    M. Kendall, J.D. Gibbons, Rank Correlation Methods, 5th edn. (Oxford University Press, 1990)Google Scholar
  3. 3.
    K.J. Arrow, Social Choice and Individual Values, 2nd edn. (Yale University Press, 1963)Google Scholar
  4. 4.
    D. Bollegala, N. Okazaki, M. Ishizuka, A machine learning approach to sentence ordering for multidocument summarization and its evaluation, in Proceedings of the Natural Language Processing society of Japan (2005)Google Scholar
  5. 5.
    C. Dwork, R. Kumar, M. Naor, D. Sivakumar, Rank aggregation methods for the Web, in Proceedings of The 10th International Conference on World Wide Web (2001), pp. 613–622Google Scholar
  6. 6.
    A. Agresti, Categorical Data Analysis, 2nd edn. (Wiley, 1996)Google Scholar
  7. 7.
    P. McCullagh, Regression models for ordinal data. J. Royal Stat. Soc. B 42(2), 109–142 (1980)MathSciNetzbMATHGoogle Scholar
  8. 8.
    A. Shashua, A. Levin, Ranking with large margin principle: Two approaches, in Advances in Neural Information Processing Systems, vol. 15 (2003), pp. 961–968Google Scholar
  9. 9.
    W.W. Cohen, R.E. Schapire, Y. Singer, Learning to order things. J. Artif. Intell. Res. 10, 243–270 (1999)MathSciNetzbMATHGoogle Scholar
  10. 10.
    M. Grötschel, M. Jünger, G. Reinelt, A cutting plane algorithm for the linear ordering problem. Oper. Res. 32(6), 1195–1220 (1984)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    N. Littlestone, Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Mach. Learn. 2, 285–318 (1988)Google Scholar
  12. 12.
    Y. Freund, R. Iyer, R.E. Schapire, Y. Singer, An efficient boosting algorithm for combining preferences, in Proceedings of The 15th International Conference on Machine Learning (1998), pp. 170–178Google Scholar
  13. 13.
    Y. Freund, R. Iyer, R.E. Schapire, Y. Singer, An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933–969 (2003)MathSciNetGoogle Scholar
  14. 14.
    H. Kazawa, T. Hirao, E. Maeda, Order SVM: a kernel method for order learning based on generalized order statistics. Syst. Comput. Jpn. 36(1), 35–43 (2005)CrossRefGoogle Scholar
  15. 15.
    R. Herbrich, T. Graepel, P. Bollmann-Sdorra, K. Obermayer, Learning preference relations for information retrieval, in ICML-98 Workshop: Text Categorization and Machine Learning (1998), pp. 80–84Google Scholar
  16. 16.
    R. Herbrich, T. Graepel, K. Obermayer, Support vector learning for ordinal regression, in Proceedings of the 9th International Conference on Artificial Neural Networks (1999), pp. 97–102Google Scholar
  17. 17.
    T. Joachims, Optimizing search engines using clickthrough data, in Proceedings of The 8th International Conference on Knowledge Discovery and Data Mining (2002), pp. 133–142Google Scholar
  18. 18.
    O. Luaces, G.F. Bayón, J.R. Quevedo, J. Díez, J.J. del Coz, A. Bahamonde, Analyzing sensory data using non-linear preference learning with feature subset selection, in Proceedings of the 15th European Conference on Machine Learning (2004), pp. 286–297 [LNAI 3201]Google Scholar
  19. 19.
    T. Kamishima, H. Kazawa, S. Akaho, Supervised ordering – an empirical survey, in Proceedings of The 5th IEEE International Conference on Data Mining (2005), pp. 673–676Google Scholar
  20. 20.
    B.C. Arnold, N. Balakrishnan, H.N. Nagaraja, A First Course in Order Statistics (Wiley, 1992)Google Scholar
  21. 21.
    L.L. Thurstone, A law of comparative judgment. Psychol. Rev. 34, 273–286 (1927)CrossRefGoogle Scholar
  22. 22.
    T. Kamishima, Nantonac collaborative filtering: Recommendation based on order responses, in Proceedings of The 9th International Conference on Knowledge Discovery and Data Mining (2003), pp. 583–588Google Scholar
  23. 23.
    T. Kamishima, J. Fujiki, Clustering orders, in Proceedings of The 6th International Conference on Discovery Science (2003), pp. 194–207 [LNAI 2843]Google Scholar
  24. 24.
    T. Kamishima, S. Akaho, Efficient clustering for orders, in Mining Complex Data, vol. 165, Studies in Computational Intelligence, ed. by D.A. Zighed, S. Tsumoto, Z.W. Ras, H. Hacid (Springer, 2009), pp. 261–280Google Scholar
  25. 25.
    D.E. Critchlow, M.A. Fligner, J.S. Verducci, Probability models on rankings. J. Math. Psychol. 35, 294–318 (1991)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    F. Mosteller, Remarks on the method of paired comparisons: I – the least squares solution assuming equal standard deviations and equal correlations. Psychometrika 16(1), 3–9 (1951)CrossRefGoogle Scholar
  27. 27.
    B. Babington Smith, Discussion on professor ross’s paper. J. Royal Stat. Soc. B 12, 53–56 (1950) (A.S.C. Ross, “Philological Probability Problems”, pp. 19–41)Google Scholar
  28. 28.
    R.A. Bradley, M.E. Terry, Rank analysis of incomplete block designs – i. The method of paired comparisons. Biometrika 39, 324–345 (1952)MathSciNetzbMATHGoogle Scholar
  29. 29.
    C.L. Mallows, Non-null ranking models, I. Biometrika 44, 114–130 (1957)MathSciNetzbMATHGoogle Scholar
  30. 30.
    R.L. Plackett, The analysis of permutations. J. Royal Stat. Soc. C 24(2), 193–202 (1975)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Toshihiro Kamishima
    • 1
  • Hideto Kazawa
    • 2
  • Shotaro Akaho
    • 1
  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)TsukubaJapan
  2. 2.Google Japan Inc.Cerulean Tower 6FSibuya-kuTokyo

Personalised recommendations