skip to main content
10.1145/1645953.1646169acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
poster

Multi-task learning for learning to rank in web search

Published:02 November 2009Publication History

ABSTRACT

Both the quality and quantity of training data have significant impact on the performance of ranking functions in the context of learning to rank for web search. Due to resource constraints, training data for smaller search engine markets are scarce and we need to leverage existing training data from large markets to enhance the learning of ranking function for smaller markets. In this paper, we present a boosting framework for learning to rank in the multi-task learning context for this purpose. In particular, we propose to learn non-parametric common structures adaptively from multiple tasks in a stage-wise way. An algorithm is developed to iteratively discover super-features that are effective for all the tasks. The estimation of the functions for each task is then learned as a linear combination of those super-features. We evaluate the performance of this multi-task learning method for web search ranking using data from a search engine. Our results demonstrate that multi-task learning methods bring significant relevance improvements over existing baseline methods.

References

  1. A. Argyriou, T. Evgeniou and M. Pontil. Multi-task feature learning. Advances in Neural Information Processing Systems 19, pages 41--48. MIT Press, Cambridge, 2007.Google ScholarGoogle Scholar
  2. B. Bakker and T. Heskes. Task clustering and gating for bayesian multitask learning. Journal of Machine Learning Research, 4:83--99, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton and G. Hullender. Learning to rank using gradient descent. In Proceedings of the 22nd International Conference on Machine learning, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Baxter. A bayesian/information theoretic model of learning to learn via multiple task sampling. Machine Learning, 28(1):7--39, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. H. Friedman. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189--1232, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  6. J. H. Friedman. Stochastic gradient boosting. Computational Statistics and Data Analysis, 38, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. K. Yu, V. Tresp and A. Schwaighofer. Learning gaussian processes from multiple tasks. ICML, volume 119 of ACM International Conference Proceeding Series, pages 1012--1019, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. K. Jäarvelin and J. Kekäaläainen. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20, 422--446. 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. R. Caruana. Multitask learning. Machine Learning, 28(1):41--75, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of ACM SIGKDD, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Y. Freund, R. D. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. In Proceedings of the Fifteenth International Conference on Machine Learning, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Z. Zheng, K. Chen, G. Sun and H. Zha. A regression framework for learning ranking functions using relative relevance judgments. In Proceedings of the 30th ACM SIGIR conference, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Multi-task learning for learning to rank in web search

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management
      November 2009
      2162 pages
      ISBN:9781605585123
      DOI:10.1145/1645953

      Copyright © 2009 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 2 November 2009

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • poster

      Acceptance Rates

      Overall Acceptance Rate1,861of8,427submissions,22%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader