Skip to main content
SpringerLink
Log in
Book cover

Preference Learning pp 203–215Cite as

Dimension Reduction for Object Ranking

  • Chapter
  • First Online:

  • 2404 Accesses

Abstract

Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results and bestseller lists. Techniques for processing such ordinal data are being developed, particularly methods for an object ranking task: i.e., learning functions used to sort objects from sample orders. In this article, we propose two dimension reduction methods specifically designed to improve prediction performance in an object ranking task.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.kamishima.net/sushi/

  2. 2.

    http://svmlight.joachims.org/

References

  1. T. Kamishima, H. Kazawa, S. Akaho, A survey and empirical comparison of object ranking methods, in Preference Learning, ed. by J. Fürnkranz, E. Hüllermeier (Springer, 2010)

    Google Scholar 

  2. T. Kamishima, H. Kazawa, S. Akaho, Supervised ordering – an empirical survey, in Proceedings of The 5th IEEE International Conference on Data Mining (2005) pp. 673–676

    Google Scholar 

  3. 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 

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

    MathSciNet  MATH  Google Scholar 

  5. 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. 80–84

    Google Scholar 

  6. T. Joachims, Optimizing search engines using clickthrough data, in Proceedings of The 8th International Conference on Knowledge Discovery and Data Mining (2002), pp. 133–142

    Google Scholar 

  7. F. Radlinski, T. Joachims, Query chains: Learning to rank from implicit feedback, in Proceedings of The 11th International Conference on Knowledge Discovery and Data Mining (2005), pp. 239–248

    Google Scholar 

  8. H. Yu, SVM selective sampling for ranking with application to data retrieval, in Proceedings of The 11th International Conference on Knowledge Discovery and Data Mining (2005), pp. 354–363

    Google Scholar 

  9. Y. Freund, R. Iyer, R.E. Schapire, Y. Singer, An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933–969 (2003)

    MathSciNet  Google Scholar 

  10. A. Bahamonde, G.F. Bayón, J.D.J.R. Quevedo, O. Luaces, J.J. del Coz, J. Alonso, F. Goyache, Feature subset selection for learning preferences: A case study, in Proceedings of The 21st International Conference on Machine Learning (2004), pp. 49–56

    Google Scholar 

  11. D.J. Slotta, J.P. Vergara, N. Ramakrishnan, L.S. Heath, Algorithms for feature selection in rank-order spaces. Technical Report TR-05-08, Computer Science, Virginia Tech. (2005)

    Google Scholar 

  12. M. Dettling, P. Bühlmann, Supervised clustering of genes. Genome Biol. 3(12) (2002). doi:research0069.1–0069.15

    Google Scholar 

  13. L. Deng, J. Pei, J. Ma, D.L. Lee, A rank sum test method for informative gene discovery, in Proceedings of The 10th International Conference on Knowledge Discovery and Data Mining (2004), pp. 410–419

    Google Scholar 

  14. T. Kamishima, S. Akaho, Filling-in missing objects in orders, in Proceedings of The 4th IEEE International Conference on Data Mining (2004), pp. 423–426

    Google Scholar 

  15. P. Diaconis, A generalization of spectral analysis with application to ranked data. Ann. Stat. 17(3), 949–979 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  16. 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)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the grants-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. We would also like to thank Thorsten Joachims for providing the { SVM}light software.

Author information

Authors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba Central 2, Umezono 1–1–1, Tsukuba, Ibaraki, 305–8568, Japan

    Toshihiro Kamishima & Shotaro Akaho

Authors
  1. Toshihiro Kamishima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shotaro Akaho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Toshihiro Kamishima .

Editor information

Editors and Affiliations

  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

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Kamishima, T., Akaho, S. (2010). Dimension Reduction for Object Ranking. In: Fürnkranz, J., Hüllermeier, E. (eds) Preference Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14125-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14125-6_10

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14124-9

  • Online ISBN: 978-3-642-14125-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation