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Hierarchical feature selection for ranking

Published: 26 April 2010 Publication History

Abstract

Ranking is an essential part of information retrieval(IR) tasks such as Web search. Nowadays there are hundreds of features for ranking. So learning to rank(LTR), an interdisciplinary field of IR and machine learning(ML), has attracted increasing attention. Those features used in the IR are not always independent from each other, hence the feature selection, an important issue in ML, should be paid attention to for LTR. However, the state-of-the-art LTR approaches merely analyze the connection among the features from the aspects of feature selection. In this paper, we propose a hierarchical feature selection strategy containing 2 phases for ranking and learn ranking functions. The experimental results show that ranking functions based on the selected feature subset significantly outperform the ones based on all features.

References

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Z. Cao and et.al. Learning to rank: from pairwise approach to listwise approach. In ICML 2007, pages 129--136.
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X. Geng and et.al. Feature selection for ranking. In SIGIR 2007, pages 407--414.
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R. Herbrich and et.al. Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers, pages 115--132, 2000.
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T. Joachims. Optimizing search engines using clickthrough data. In KDD 2002, pages 133--142.
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T.-Y. Liu. Learning to rank for information retrieval. In Foundation and Trends on Information Retrieval, pages 641--647, 2009.
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F. Pan and et.al. Feature selection for ranking using boosted trees. In CIKM 2009, pages 2025--2028.
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M. Zhang and et.al. Is learning to rank effective for web search. In SIGIR 2009 workshop: Learning to Rank for Information Retrieval, pages 641--647.

Cited By

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  • (2022)A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysisComputer Science and Information Systems10.2298/CSIS201220042Y19:1(141-164)Online publication date: 2022
  • (2021)Dynamic, Incremental, and Continuous Detection of Cyberbullying in Online Social MediaACM Transactions on the Web10.1145/344801415:3(1-33)Online publication date: 13-May-2021
  • (2020)Graph-based Feature Selection Method for Learning to RankProceedings of the 6th International Conference on Communication and Information Processing10.1145/3442555.3442567(70-73)Online publication date: 27-Nov-2020
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  1. Hierarchical feature selection for ranking

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    Published In

    cover image ACM Other conferences
    WWW '10: Proceedings of the 19th international conference on World wide web
    April 2010
    1407 pages
    ISBN:9781605587998
    DOI:10.1145/1772690

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 April 2010

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    Author Tags

    1. feature selection
    2. learning to rank

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    WWW '10
    WWW '10: The 19th International World Wide Web Conference
    April 26 - 30, 2010
    North Carolina, Raleigh, USA

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    View all
    • (2022)A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysisComputer Science and Information Systems10.2298/CSIS201220042Y19:1(141-164)Online publication date: 2022
    • (2021)Dynamic, Incremental, and Continuous Detection of Cyberbullying in Online Social MediaACM Transactions on the Web10.1145/344801415:3(1-33)Online publication date: 13-May-2021
    • (2020)Graph-based Feature Selection Method for Learning to RankProceedings of the 6th International Conference on Communication and Information Processing10.1145/3442555.3442567(70-73)Online publication date: 27-Nov-2020
    • (2019)Cyberbullying Ends Here: Towards Robust Detection of Cyberbullying in Social MediaThe World Wide Web Conference10.1145/3308558.3313462(3427-3433)Online publication date: 13-May-2019
    • (2019)Machine Learning Methods for RankingInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819401930001X29:06(729-761)Online publication date: 25-Jun-2019
    • (2019)Deep Neural Network Regularization for Feature Selection in Learning-to-RankIEEE Access10.1109/ACCESS.2019.29026407(53988-54006)Online publication date: 2019
    • (2019)Non-linear analysis of bursty workloads using dual metrics for better cloud resource managementJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01183-8Online publication date: 8-Jan-2019
    • (2018)A Systematic Study of Feature Selection Methods for Learning to Rank AlgorithmsInternational Journal of Information Retrieval Research10.4018/IJIRR.20180701048:3(46-67)Online publication date: 1-Jul-2018
    • (2018)MOFSRankComplexity10.1155/2018/78376962018Online publication date: 1-Jan-2018
    • (2016)From Tf-Idf to Learning-to-RankBusiness Intelligence10.4018/978-1-4666-9562-7.ch063(1245-1292)Online publication date: 2016
    • Show More Cited By

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