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A Boosting Approach for Learning to Rank Using SVD with Partially Labeled Data

  • Yuan Lin
  • Hongfei Lin
  • Zhihao Yang
  • Sui Su
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5839)

Abstract

Learning to rank has become a hot issue in the community of information retrieval. It combines the relevance judgment information with the approaches of both in information retrieval and machine learning, so as to learn a more accurate ranking function for retrieval. Most previous approaches only rely on the labeled relevance information provided, thus suffering from the limited training data size available. In this paper, we try to use Singular Value Decomposition (SVD) to utilize the unlabeled data set to extract new feature vectors, which are then embedded in a RankBoost leaning framework. We experimentally compare the performance of our approach against that without incorporating new features generated by SVD. The experimental results show that our approach can consistently improve retrieval performance across several LETOR data sets, thus indicating effectiveness of new SVD generated features for learning ranking function.

Keywords

Information Retrieval Learning to rank Machine learning SVD 

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References

  1. 1.
    Freund, Y., Iyer, R., Schapire, R., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4, 933–969 (2003)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: International Conference on Artificial Neural Networks, Edinburgh, UK, vol. 1, pp. 97–102 (1999)Google Scholar
  3. 3.
    Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., Li, H.: Learning to Rank: From Pairwise Approach to Listwise Approach. In: International conference on Machine learning, Corvalis, Oregon, USA, pp. 129–136 (2007)Google Scholar
  4. 4.
    Guiver, J., Snelson, E.: Learning to Rank with SoftRank and Gaussian Processes. In: ACM Special Interest Group on Information Retrieval, Singapore, pp. 259–266 (2008)Google Scholar
  5. 5.
    Veloso, A., Almeida, H.: Learning to Rank at Query-Time using Association Rules. In: ACM Special Interest Group on Information Retrieval, Singapore, pp. 267–274 (2008)Google Scholar
  6. 6.
    Amini, M.-R., Truong, T.-V., Goutte, C.: A Boosting Algorithm for Learning Bipartite Ranking Functions with Partially Labeled Data. In: ACM Special Interest Group on Information Retrieval, Singapore, pp. 99–106 (2008)Google Scholar
  7. 7.
    Duh, K., Kirchhoff, K.: Learning to Rank with Partially-Labeled Data. In: ACM Special Interest Group on Information Retrieval, Singapore, pp. 251–258 (2008)Google Scholar
  8. 8.
    Blitzer, J., McDonald, R., Pereira, F.: Domain Adaptation with Structural Correspondence Learning. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, Australia, pp. 120–128 (2006)Google Scholar
  9. 9.
    Liu, T.-Y., Qin, T., Xu, J., Xiong, W., Li, H.: LETOR: Benchmark dataset for research on learning to rank for information retrieval. In: SIGIR 2007 Workshop on Learning to Rank for IR, ACM SIGIR Forum, vol. 41(2), pp. 58–62 (2007)Google Scholar
  10. 10.
    Robertson, S.E.: Overview of the okapi projects. Journal of Documentation 53(1), 3–7 (1997)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kleinberg, J.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–622 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the Web, Technical report. Stanford University (1998)Google Scholar
  13. 13.
    Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to Ad Hoc information retrieval. In: Proceedings of SIGIR 2001, pp. 334–342 (2001)Google Scholar
  14. 14.
    Lin, H.-F., YAO, T.-S.: Text Browsing Based on Latent Semantic Indexing. Journal of Chinese Information Processing 14(5), 49–56 (2000)Google Scholar
  15. 15.
    Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: The 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, USA, pp. 41–48 (2000)Google Scholar
  16. 16.
    Zhou, K., Xue, G.-R., Zha, H.-Y., Yu, Y.: Learning to Rank with Ties. In: ACM Special Interest Group on Information Retrieval, Singapore, pp. 275–282 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yuan Lin
    • 1
  • Hongfei Lin
    • 1
  • Zhihao Yang
    • 1
  • Sui Su
    • 1
  1. 1.Department of Computer Science and EngineeringDalian University of TechnologyDalianChina

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