Article

Using RankBoost to compare retrieval systems

Authors:

Huyen-Trang Vu,

Patrick GallinariAuthors Info & Claims

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Pages 309 - 310

https://doi.org/10.1145/1099554.1099641

Published: 31 October 2005 Publication History

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Abstract

This paper presents a new pooling method for constructing the assessment sets used in the evaluation of retrieval systems. Our proposal is based on RankBoost, a machine learning voting algorithm. It leads to smaller pools than classical pooling and thus reduces the manual assessment workload for building test collections. Experimental results obtained on an XML document collection demonstrate the effectiveness of the approach according to different evaluation criteria.

References

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J. A. Aslam, V. Pavlu, and R. Savell. A unified model for metasearch, pooling, and system evaluation. In CIKM'03, pages 484--491, 2003.

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Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4:933--969, Nov. 2003.

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B. Piwowarski and M. Lalmas. Providing Consistent and Exhaustive Relevance Assessments for XML Retrieval Evaluation. In CIKM'04, Nov. 2004.

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He QLi XSun Y(2021)Company Ranking Prediction Based on Network Big DataIETE Journal of Research10.1080/03772063.2021.198614469:9(6176-6187)Online publication date: 19-Oct-2021
https://doi.org/10.1080/03772063.2021.1986144
Zhang HJiang ZXiong QWu JYuan TLi GHuang YJi D(2020)Gathering Effective Information for Real-Time Material RecognitionIEEE Access10.1109/ACCESS.2020.30203828(159511-159529)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3020382
Zhang HQiu DWu RDeng YJi DLi T(2019)Novel framework for image attribute annotation with gene selection XGBoost algorithm and relative attribute modelApplied Soft Computing10.1016/j.asoc.2019.03.017Online publication date: Mar-2019
https://doi.org/10.1016/j.asoc.2019.03.017
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Using RankBoost to compare retrieval systems

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Published In

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

October 2005

854 pages

ISBN:1595931406

DOI:10.1145/1099554

General Chair:
Otthein Herzog
University of Bremen, Germany
,
Program Chairs:
Hans-Jörg Schek
University for Health Sciences, Medical Informatics and Technology, Austria
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Abdur Chowdhury
America Online, USA
,
Wilfried Teiken
IBM T.J. Watson Research Center, USA

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

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Published: 31 October 2005

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October 31 - November 5, 2005

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CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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He QLi XSun Y(2021)Company Ranking Prediction Based on Network Big DataIETE Journal of Research10.1080/03772063.2021.198614469:9(6176-6187)Online publication date: 19-Oct-2021
https://doi.org/10.1080/03772063.2021.1986144
Zhang HJiang ZXiong QWu JYuan TLi GHuang YJi D(2020)Gathering Effective Information for Real-Time Material RecognitionIEEE Access10.1109/ACCESS.2020.30203828(159511-159529)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3020382
Zhang HQiu DWu RDeng YJi DLi T(2019)Novel framework for image attribute annotation with gene selection XGBoost algorithm and relative attribute modelApplied Soft Computing10.1016/j.asoc.2019.03.017Online publication date: Mar-2019
https://doi.org/10.1016/j.asoc.2019.03.017
Clémençon SLugosi GVayatis N(2008)Ranking and Empirical Minimization of U-statisticsThe Annals of Statistics10.1214/00905260700000091036:2Online publication date: 1-Apr-2008
https://doi.org/10.1214/009052607000000910
Piwowarski BTrotman ALalmas M(2008)Sound and complete relevance assessment for XML retrievalACM Transactions on Information Systems (TOIS)10.1145/1416950.141695127:1(1-37)Online publication date: 23-Dec-2008
https://dl.acm.org/doi/10.1145/1416950.1416951

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A Mathematical Information Retrieval System Based on RankBoost

Rankboost+: an improvement to Rankboost

Distributed RankBoost Acceleration Using FPGA and MPI for Web Relevance Ranking

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Rankboost $+$ : an improvement to Rankboost