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A two-stage mixture model for pseudo feedback

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SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 486 - 487

https://doi.org/10.1145/1008992.1009083

Published: 25 July 2004 Publication History

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Abstract

Pseudo feedback is a commonly used technique to improve information retrieval performance. It assumes a few top-ranked documents to be relevant, and learns from them to improve the retrieval accuracy. A serious problem is that the performance is often very sensitive to the number of pseudo feedback documents. In this poster, we address this problem in a language modeling framework. We propose a novel two-stage mixture model, which is less sensitive to the number of pseudo feedback documents than an effective existing feedback model. The new model can tolerate a more flexible setting of the number of pseudo feedback documents without the danger of losing much retrieval accuracy.

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T. Tao and C. Zhai. A new mixture clustering model for pseudo feedback. In The 2004 Meeting of the International Federation of Classification Societies, 2004.

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Cited By

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Zhao HSi LLi XZhang QKando NSakai TJoho HLi Hde Vries AWhite R(2017)Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model ApproachProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080676(909-912)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080676
Dehghani MAzarbonyad HKamps JHiemstra DMarx MMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Luhn RevisitedProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983814(1301-1310)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983814
Yan RGao G(2016)A Two-Stage Ranking Scheme for Pseudo Relevance Feedback2016 3rd International Conference on Information Science and Control Engineering (ICISCE)10.1109/ICISCE.2016.38(129-133)Online publication date: Jul-2016
https://doi.org/10.1109/ICISCE.2016.38
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Index Terms

A two-stage mixture model for pseudo feedback
1. Information systems
  1. Information retrieval

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Published In

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

July 2004

624 pages

ISBN:1581138814

DOI:10.1145/1008992

General Chair:
Mark Sanderson
University of Sheffield (UK)
,
Program Chairs:
Kalervo Järvelin
University of Tampere (Finland)
,
James Allan
University of Massachusetts (USA)
,
Peter Bruza
Distributed Systems Technology Centre (Australia)

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Association for Computing Machinery

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Published: 25 July 2004

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SIGIR04: The 27th ACM/SIGIR International Symposium on Information Retrieval 2004

July 25 - 29, 2004

Sheffield, United Kingdom

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

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Zhao HSi LLi XZhang QKando NSakai TJoho HLi Hde Vries AWhite R(2017)Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model ApproachProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080676(909-912)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080676
Dehghani MAzarbonyad HKamps JHiemstra DMarx MMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Luhn RevisitedProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983814(1301-1310)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983814
Yan RGao G(2016)A Two-Stage Ranking Scheme for Pseudo Relevance Feedback2016 3rd International Conference on Information Science and Control Engineering (ICISCE)10.1109/ICISCE.2016.38(129-133)Online publication date: Jul-2016
https://doi.org/10.1109/ICISCE.2016.38
Liu NWang JLiu G(2012)Query Expansion Based on Mongolian SemanticsProceedings of the 2012 Third World Congress on Software Engineering10.1109/WCSE.2012.13(25-28)Online publication date: 6-Nov-2012
https://dl.acm.org/doi/10.1109/WCSE.2012.13
Yamamoto MMase HYajima HKinukawa H(2010)A Journal Paper Filtering Using the Profile Revised by Patent Document Informationプロファイルに特許文書情報を加味した学術論文フィルタリング方式IEEJ Transactions on Electronics, Information and Systems10.1541/ieejeiss.130.358130:2(358-366)Online publication date: 2010
https://doi.org/10.1541/ieejeiss.130.358
Yamamoto MMase HYajima HKinukawa H(2010)Paper filtering method using features of coauthor research group, subject category, and terminologyElectronics and Communications in Japan10.1002/ecj.1026493:9(1-11)Online publication date: 24-Aug-2010
https://doi.org/10.1002/ecj.10264
Cao HJiang DPei JChen ELi HQuemada JLeón GMaarek YNejdl W(2009)Towards context-aware search by learning a very large variable length hidden markov model from search logsProceedings of the 18th international conference on World wide web10.1145/1526709.1526736(191-200)Online publication date: 20-Apr-2009
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Li XZhu Z(2008)Enhancing relevance models with adaptive passage retrievalProceedings of the IR research, 30th European conference on Advances in information retrieval10.5555/1793274.1793331(463-471)Online publication date: 30-Mar-2008
https://dl.acm.org/doi/10.5555/1793274.1793331
Bekkerman RCrammer KLapata MNg H(2008)One-class clustering in the text domainProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/1613715.1613722(41-50)Online publication date: 25-Oct-2008
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Yamamoto MMase HYajima HKinukawa H(2008)Paper Filtering Method Using Features of Co-Author Research Group, Subject Category and Terminology著者・分野・用語の特性を利用した論文フィルタリング方式IEEJ Transactions on Electronics, Information and Systems10.1541/ieejeiss.128.1358128:8(1358-1366)Online publication date: 2008
https://doi.org/10.1541/ieejeiss.128.1358
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Regularized estimation of mixture models for robust pseudo-relevance feedback

A Clustering Approach to Improving Pseudo-Relevance Feedback: Improving Retrieval Effetiveness by Removing Noisy Documents

Query dependent pseudo-relevance feedback based on wikipedia

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