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Learning to rank collections

Published: 23 July 2007 Publication History

Abstract

Collection selection, ranking collections according to user query is crucial in distributed search. However, few features are used to rank collections in the current collection selection methods, while hundreds of features are exploited to rank web pages in web search. The lack of features affects the efficiency of collection selection in distributed search. In this paper, we exploit some new features and learn to rank collections with them through SVM and RankingSVM respectively. Experimental results show that our features are beneficial to collection selection, and the learned ranking functions outperform the classical CORI algorithm.

References

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C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of ICML'05, 2005, 89--96.
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J. P. Callan, Z. Lu, and W. B. Croft. Searching distributed collections with inference networks. In Proceedings of SIGIR '95, 1995, 21--28.
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Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res., 2003, 4:933--969.
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P. G. Ipeirotis and L. Gravano. Distributed search over the hidden web: hierarchical database sampling and selection. In Proceedings of VLDB '02, 2002.
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T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of SIGKDD '02, 2002, 133--142.
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L. Si and J. Callan. Relevant document distribution estimation method for resource selection. In Proceedings of SIGIR'03, 2003, 289--305.

Cited By

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  • (2023)Federated search techniques: an overview of the trends and state of the artKnowledge and Information Systems10.1007/s10115-023-01922-665:12(5065-5095)Online publication date: 10-Jul-2023
  • (2019)LTRRS: A Learning to Rank Based Algorithm for Resource Selection in Distributed Information RetrievalInformation Retrieval10.1007/978-3-030-31624-2_5(52-63)Online publication date: 18-Sep-2019
  • (2010)Vertical selection in the presence of unlabeled verticalsProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval10.1145/1835449.1835564(691-698)Online publication date: 19-Jul-2010
  • Show More Cited By

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    cover image ACM Conferences
    SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
    July 2007
    946 pages
    ISBN:9781595935977
    DOI:10.1145/1277741
    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]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 23 July 2007

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    Author Tags

    1. collection selection
    2. distributed search

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    SIGIR07
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    SIGIR07: The 30th Annual International SIGIR Conference
    July 23 - 27, 2007
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

    View all
    • (2023)Federated search techniques: an overview of the trends and state of the artKnowledge and Information Systems10.1007/s10115-023-01922-665:12(5065-5095)Online publication date: 10-Jul-2023
    • (2019)LTRRS: A Learning to Rank Based Algorithm for Resource Selection in Distributed Information RetrievalInformation Retrieval10.1007/978-3-030-31624-2_5(52-63)Online publication date: 18-Sep-2019
    • (2010)Vertical selection in the presence of unlabeled verticalsProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval10.1145/1835449.1835564(691-698)Online publication date: 19-Jul-2010
    • (2009)Generative model-based metasearch for data fusion in information retrievalProceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries10.1145/1555400.1555426(153-162)Online publication date: 15-Jun-2009

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