research-article

A unified optimization framework for robust pseudo-relevance feedback algorithms

Authors:

Joshua V. Dillon,

Kevyn Collins-ThompsonAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 1069 - 1078

https://doi.org/10.1145/1871437.1871573

Published: 26 October 2010 Publication History

Abstract

We present a flexible new optimization framework for finding effective, reliable pseudo-relevance feedback models that unifies existing complementary approaches in a principled way. The result is an algorithmic approach that not only brings together different benefits of previous methods, such as parameter self-tuning and risk reduction from term dependency modeling, but also allows a rich new space of model search strategies to be investigated. We compare the effectiveness of a unified algorithm to existing methods by examining iterative performance and risk-reward tradeoffs. We also discuss extensions for generating new algorithms within our framework.

References

[1]

S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, USA, 2004.

Digital Library

[2]

K. Collins-Thompson. Accounting for stability of retrieval algorithms using risk-reward curves. In Proceedings of SIGIR 2009 Workshop on the Future of Evaluation in Information Retrieval, pages 27--28.

[3]

K. Collins-Thompson. Reducing the risk of query expansion via robust constrained optimization. In Proceedings of CIKM 2009, pages 837--846.

Digital Library

[4]

K. Collins-Thompson. Estimating robust query models using convex optimization. In Advances in Neural Information Processing Systems 21 (NIPS), 2008.

[5]

K. Collins-Thompson. Robust model estimation methods for information retrieval. PhD thesis, Carnegie Mellon Univ., 2008.

[6]

K. Collins-Thompson and J. Callan. Query expansion using random walk models. In CIKM 2005, pages 704--711.

Digital Library

[7]

T. M. Cover and J. A. Thomas. Elements of Information Theory. John Wiley & Sons, second edition, 2005.

[8]

F. Diaz. Autocorrelation and Regularization of Query-Based Information Retrieval Scores. PhD thesis, University of Massachusetts, Amherst, 2007.

Digital Library

[9]

J. Dillon, Y. Mao, G. Lebanon, and J. Zhang. Statistical translation, heat kernels, and expected distances. In Proc. of UAI 2007, 2007.

[10]

M. Figueirdo, J. Bioucas-Dias, and R. Nowak. Majorization-minimization algorithms for wavelet-based image restoration. IEEE Transactions on Image Processing, 16(12):2980--2991, 2007.

Digital Library

[11]

J. Graça, K. Ganchev, and B. Taskar. Expectation maximization and posterior constraints. In NIPS, 2007.

[12]

D. Harman and C. Buckley. The NRRC Reliable Information Access (RIA) workshop. In Proceedings of SIGIR 2004, pages 528--529, New York, USA, 2004.

Digital Library

[13]

V. Lavrenko. A Generative Theory of Relevance. PhD thesis, University of Massachusetts, Amherst, 2004.

Digital Library

[14]

Lemur. Lemur toolkit for language modeling & retrieval. 2002. http://www.lemurproject.org.

[15]

Q. Mei, D. Zhang, and C. Zhai. A general optimization framework for smoothing language models on graph structures. In Proceedings of SIGIR 2008, pages 611--618, 2008.

Digital Library

[16]

R. Neal and G. E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models, 1998.

Digital Library

[17]

J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In Proceedings of SIGIR 1998, pages 275--281, 1998.

Digital Library

[18]

B. Sriperumbudur and G. Lanckriet. On the convergence of the concave-convex procedure. In NIPS 22. Cambridge, MA: MIT Press, 2009.

[19]

T. Strohman, D. Metzler, H. Turtle, and W. B. Croft. Indri: A language model-based search engine for complex queries. In Proc. Int. Conf. on Intel. Analysis, 2004.

[20]

T. Tao and C. Zhai. Regularized estimation of mixture models for robust pseudo-relevance feedback. In SIGIR 2006, pages 162--169.

Digital Library

[21]

R. Udupa, A. Bhole, and P. Bhattacharya. A term is known by the company it keeps: On selecting a good expansion set in pseudo-relevance feedback. In Proceedings of ICTIR 2009, Advances in IR Theory. Springer, 2009.

Digital Library

[22]

Z. Xu and R. Akella. A new probabilistic retrieval model based on the Dirichlet compound multinomial distribution. In SIGIR 2008, pages 427--434, 2008.

Digital Library

[23]

C.-N. J. Yu and T. Joachims. Learning structural SVMs with latent variables. In International Conference on Machine Learning (ICML), 2009.

Digital Library

[24]

A. L. Yuille and A. Rangarajan. The Concave-Convex Procedure (CCCP). In NIPS 2001, pages 1033--1040, 2001.

Cited By

Lilian JSundarakantham KShalinie S(2021)QeCSO: Design of hybrid Cuckoo Search based Query expansion model for efficient information retrievalSādhanā10.1007/s12046-021-01706-046:3Online publication date: 4-Sep-2021
https://doi.org/10.1007/s12046-021-01706-0
Kumar RBhanodai GPamula R(2019)Book search using social information, user profiles and query expansion with Pseudo Relevance FeedbackApplied Intelligence10.1007/s10489-018-1383-z49:6(2178-2200)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1007/s10489-018-1383-z
Chen KLiu SChen BWang HChen H(2016)Exploring the use of unsupervised query modeling techniques for speech recognition and summarizationSpeech Communication10.1016/j.specom.2016.03.00680:C(49-59)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.specom.2016.03.006
Show More Cited By

Index Terms

A unified optimization framework for robust pseudo-relevance feedback algorithms
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

Copyright © 2010 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]

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Publication History

Published: 26 October 2010

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October 26 - 30, 2010

ON, Toronto, Canada

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Cited By

Lilian JSundarakantham KShalinie S(2021)QeCSO: Design of hybrid Cuckoo Search based Query expansion model for efficient information retrievalSādhanā10.1007/s12046-021-01706-046:3Online publication date: 4-Sep-2021
https://doi.org/10.1007/s12046-021-01706-0
Kumar RBhanodai GPamula R(2019)Book search using social information, user profiles and query expansion with Pseudo Relevance FeedbackApplied Intelligence10.1007/s10489-018-1383-z49:6(2178-2200)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1007/s10489-018-1383-z
Chen KLiu SChen BWang HChen H(2016)Exploring the use of unsupervised query modeling techniques for speech recognition and summarizationSpeech Communication10.1016/j.specom.2016.03.00680:C(49-59)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.specom.2016.03.006
Khennak IDrias H(2016)A Firefly Algorithm-based Approach for Pseudo-Relevance FeedbackJournal of Medical Systems10.1007/s10916-016-0603-540:11(1-15)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s10916-016-0603-5
Lin WChen ZKe STsai CLin W(2015)The effect of low-level image features on pseudo relevance feedbackNeurocomputing10.1016/j.neucom.2015.04.037166:C(26-37)Online publication date: 20-Oct-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.04.037
Wang ZZhang M(2015)Feedback Model for Microblog RetrievalDatabase Systems for Advanced Applications10.1007/978-3-319-18120-2_31(529-544)Online publication date: 9-Apr-2015
https://doi.org/10.1007/978-3-319-18120-2_31
Tsai CHu YChen Z(2015)Factors affecting rocchio-based pseudorelevance feedback in image retrievalJournal of the Association for Information Science and Technology10.1002/asi.2315466:1(40-57)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1002/asi.23154
Lv YZhai CLi JWang XGarofalakis MSoboroff ISuel TWang M(2014)Revisiting the Divergence Minimization Feedback ModelProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2661900(1863-1866)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2661900
Zhang PSong DWang JHou Y(2014)Bias-variance analysis in estimating true query model for information retrievalInformation Processing and Management: an International Journal10.1016/j.ipm.2013.08.00450:1(199-217)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1016/j.ipm.2013.08.004
Clinchant SGaussier E(2013)A Theoretical Analysis of Pseudo-Relevance Feedback ModelsProceedings of the 2013 Conference on the Theory of Information Retrieval10.1145/2499178.2499179(6-13)Online publication date: 29-Sep-2013
https://dl.acm.org/doi/10.1145/2499178.2499179
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