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Construct Weak Ranking Functions for Learning Linear Ranking Function

  • Guichun Hua
  • Min Zhang
  • Yiqun Liu
  • Shaoping Ma
  • Hang Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7097)

Abstract

Many Learning to Rank models, which apply machine learning techniques to fuse weak ranking functions and enhance ranking performances, have been proposed for web search. However, most of the existing approaches only apply the Min – Max normalization method to construct the weak ranking functions without considering the differences among the ranking features. Ranking features, such as the content-based feature BM25 and link-based feature PageRank, are different from each other in many aspects. And it is unappropriate to apply an uniform method to construct weak ranking functions from ranking features. In this paper, comparing the three frequently used normalization methods: Min – Max, Log, Arctan normalization, we analyze the differences among three normalization methods when constructing the weak ranking functions, and propose two normalization selection methods to decide which normalization should be used for a specific ranking feature. The experimental results show that the final ranking functions based on normalization selection methods significantly outperform the original one.

Keywords

Learning to Rank Weak Ranking Function Final Ranking Function Normalization 

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References

  1. 1.
    Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 621–630. ACM, New York (2009)Google Scholar
  2. 2.
    Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933–969 (2003)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Herbrich, R., et al.: Large margin rank boundaries for ordinal regression. In: Advances in Large Margin Classifiers, pp. 115–132 (2000)Google Scholar
  4. 4.
  5. 5.
  6. 6.
  7. 7.
    Järvelin, K., Kekäläinen, J.: Ir evaluation methods for retrieving highly relevant documents. In: SIGIR 2000: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 41–48. ACM, New York (2000)Google Scholar
  8. 8.
    Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques, vol. 20, pp. 422–446. ACM, New York (2002)Google Scholar
  9. 9.
    Joachims, T.: Optimizing search engines using clickthrough data. In: KDD 2002: Proceedings of the Eighth ACM SIGKDD Internatiounal Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM, New York (2002)Google Scholar
  10. 10.
    Liu, T.-Y.: Learning to rank for information retrieval. In: Foundation and Trends on Information Retrieval, pp. 641–647 (2009)Google Scholar
  11. 11.
    Qin, T., Liu, T.-Y., Xu, J., Li, H.: Letor: A benchmark collection for research on learning to rank for information retrieval. Information Retrieval Journal 13, 346–374 (2010)CrossRefGoogle Scholar
  12. 12.
    Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res. 6, 1453–1484 (2005)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Xia, F., Liu, T.-Y., Wang, J., Zhang, W., Li, H.: Listwise approach to learning to rank: theory and algorithm. In: ICML 2008: Proceedings of the 25th International Conference on Machine Learning, pp. 1192–1199. ACM, New York (2008)CrossRefGoogle Scholar
  14. 14.
    Yue, Y., Finley, T., Radlinski, F., Joachims, T.: A support vector method for optimizing average precision. In: SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 271–278. ACM, New York (2007)Google Scholar
  15. 15.
    Zhang, M., et al.: Is learning to rank effective for web search. In: SIGIR 2009 Workshop: Learning to Rank for Information Retrieval, pp. 641–647 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guichun Hua
    • 1
    • 2
    • 3
  • Min Zhang
    • 1
    • 2
    • 3
  • Yiqun Liu
    • 1
    • 2
    • 3
  • Shaoping Ma
    • 1
    • 2
    • 3
  • Hang Yin
    • 1
    • 2
    • 3
  1. 1.State Key Laboratory of Intelligent Technology and SystemsTsinghua UniversityBeijingChina
  2. 2.Tsinghua National Laboratory for Information Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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