poster

The search duel: a response to a strong ranker

Authors:

Peter Izsak,

Fiana Raiber,

Oren Kurland,

Moshe TennenholtzAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 919 - 922

https://doi.org/10.1145/2600428.2609474

Published: 03 July 2014 Publication History

Get Access

Abstract

How can a search engine with a relatively weak relevance ranking function compete with a search engine that has a much stronger ranking function? This dual challenge, which to the best of our knowledge has not been addressed in previous work, entails an interesting bi-modal utility function for the weak search engine. That is, the goal is to produce in response to a query a document result list whose effectiveness does not fall much behind that of the strong search engine; and, which is quite different than that of the strong engine. We present a per-query algorithmic approach that leverages fundamental retrieval principles such as pseudo-feedback-based relevance modeling. We demonstrate the merits of our approach using TREC data.

References

[1]

J. Callan. Distributed information retrieval. In W. Croft, editor, Advances in information retrieval, chapter 5, pages 127--150. Kluwer Academic Publishers, 2000.

Google Scholar

[2]

J. G. Carbonell and J. Goldstein. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proc. of SIGIR, pages 335--336, 1998.

Digital Library

Google Scholar

[3]

G. V. Cormack, C. L. A. Clarke, and S. Büttcher. Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In Proc. of SIGIR, pages 758--759, 2009.

Digital Library

Google Scholar

[4]

W. B. Croft. Combining approaches to information retrieval. In W. Croft, editor, Advances in information retrieval, chapter 1, pages 1--36. Kluwer Academic Publishers, 2000.

Google Scholar

[5]

S. Cronen-Townsend, Y. Zhou, and W. B. Croft. A language modeling framework for selective query expansion. Technical Report IR-338, Center for Intelligent Information Retrieval, University of Massachusetts, 2004.

Crossref

Google Scholar

[6]

E. A. Fox and J. A. Shaw. Combination of multiple searches. In Proc. of TREC-2, 1994.

Google Scholar

[7]

W. Gao, J. Blitzer, and M. Zhou. Using english information in non-english web search. In Proc. of the 2nd ACM workshop on Improving Non English Web Searching, iNEWS, pages 17--24, 2008.

Digital Library

Google Scholar

[8]

W. Gao, P. Cai, K.-F. Wong, and A. Zhou. Learning to rank only using training data from related domain. In Proc. of SIGIR, pages 162--169, 2010.

Digital Library

Google Scholar

[9]

N. Immorlica, A. T. Kalai, B. Lucier, A. Moitra, A. Postlewaite, and M. Tennenholtz. Dueling algorithms. In Proc. of STOC, pages 215--224, 2011.

Digital Library

Google Scholar

[10]

V. Lavrenko and W. B. Croft. Relevance-based language models. In Proc. of SIGIR, pages 120--127, 2001.

Digital Library

Google Scholar

[11]

L. Meister, O. Kurland, and I. G. Kalmanovich. Re-ranking search results using an additional retrieved list. Information Retrieval, 14(4):413--437, 2011.

Digital Library

Google Scholar

[12]

R. L. T. Santos, C. Macdonald, and I. Ounis. Exploiting query reformulations for web search result diversification. In Proc. of WWW, pages 881--890, 2010.

Digital Library

Google Scholar

Cited By

View all

Wu CZhang RGuo JDe Rijke MFan YCheng X(2023)PRADA: Practical Black-box Adversarial Attacks against Neural Ranking ModelsACM Transactions on Information Systems10.1145/357692341:4(1-27)Online publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1145/3576923
Kurland OTennenholtz MAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Competitive SearchProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532771(2838-2849)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532771
Ben-Porat OTennenholtz M(2017)Shapley Facility Location GamesWeb and Internet Economics10.1007/978-3-319-71924-5_5(58-73)Online publication date: 25-Nov-2017
https://doi.org/10.1007/978-3-319-71924-5_5
Show More Cited By

Index Terms

The search duel: a response to a strong ranker
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

Search engine competition with a knowledge-sharing service

This study considers the competition between an inferior search engine and a superior search engine with the option to introduce a knowledge-sharing service. This study focuses on the pure strategy Nash equilibrium of the competition between inferior ...
Google Search Engine: Seo Tools You Need to Explode Your Website Traffic - Google Seo, Google Ranking
Identifying popular search goals behind search queries to improve web search ranking
AIRS'11: Proceedings of the 7th Asia conference on Information Retrieval Technology

Web users usually have a certain search goal before they submit a search query. However, many laypersons can't transform their search goals into suitable queries. Thus, understanding original search goals behind a query is very important for search ...

Comments

Information & Contributors

Information

Published In

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

SIGIR '14

Sponsor:

SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

Acceptance Rates

SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

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

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
227
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)3

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wu CZhang RGuo JDe Rijke MFan YCheng X(2023)PRADA: Practical Black-box Adversarial Attacks against Neural Ranking ModelsACM Transactions on Information Systems10.1145/357692341:4(1-27)Online publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1145/3576923
Kurland OTennenholtz MAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Competitive SearchProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532771(2838-2849)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532771
Ben-Porat OTennenholtz M(2017)Shapley Facility Location GamesWeb and Internet Economics10.1007/978-3-319-71924-5_5(58-73)Online publication date: 25-Nov-2017
https://doi.org/10.1007/978-3-319-71924-5_5
Roitman HCohen DHummel SCarterette BFang HLalmas MNie J(2016)From "More Like This" to "Better Than This"Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval10.1145/2970398.2970421(195-198)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2970398.2970421
Ben Basat RTennenholtz MKurland OAllan JCroft Bde Vries AZhai C(2015)The Probability Ranking Principle is Not Optimal in Adversarial Retrieval SettingsProceedings of the 2015 International Conference on The Theory of Information Retrieval10.1145/2808194.2809456(51-60)Online publication date: 27-Sep-2015
https://dl.acm.org/doi/10.1145/2808194.2809456

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Search engine competition with a knowledge-sharing service

Google Search Engine: Seo Tools You Need to Explode Your Website Traffic - Google Seo, Google Ranking

Identifying popular search goals behind search queries to improve web search ranking

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations