research-article

Analysis of long queries in a large scale search log

Authors:

Michael Bendersky,

W. Bruce CroftAuthors Info & Claims

WSCD '09: Proceedings of the 2009 workshop on Web Search Click Data

Pages 8 - 14

https://doi.org/10.1145/1507509.1507511

Published: 09 February 2009 Publication History

Abstract

We propose to use the search log to study long queries, in order to understand the types of information needs that are behind them, and to design techniques to improve search effectiveness when they are used. Long queries arise in many different applications, such as CQA (community-based question answering) and literature search, and they have been studied to some extent using TREC data. They are also, however, quite common in web search, as can be seen by looking at the distribution of query lengths in a large scale search log.

In this paper we analyze the long queries in the search log with the aim of identifying the characteristics of the most commonly occurring types of queries, and the issues involved with using them effectively in a search engine. In addition, we propose a simple yet effective method for evaluating the performance of the queries in the search log using a combination of the click data in the search log with the existing TREC corpora.

References

[1]

E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In Proceedings of SIGIR, pages 19--26, 2006.

Digital Library

[2]

F. Ahmad and G. Kondrak. Learning a spelling error model from search query logs. In Proceedings of HLT, pages 955--962, 2005.

Digital Library

[3]

J. Allan, B. Carterette, J. Aslam, V. Pavlu, B. Dachev, and E. Kanoulas. Million query track 2007 overview. In Proceedings of TREC, 2008.

[4]

M. Bendersky and W. B. Croft. Discovering key concepts in verbose queries. In Proceedings of SIGIR, pages 491--498, 2008.

Digital Library

[5]

S. Bergsma and Q. Wang. Learning Noun Phrase Query Segmentation. In Proceedings of EMNLP-CoNLL, pages 819--826, 2007.

[6]

J. Boyan, D. Freitag, and T. Joachims. A machine learning architecture for optimizing web search engines. In Proceedings of AAAI, volume 264, 1996.

[7]

A. Broder. A taxonomy of web search. SIGIR Forum, 36(2):3--10, 2002.

Digital Library

[8]

S. Cucerzan and E. Brill. Spelling correction as an iterative process that exploits the collective knowledge of web users. In Proceedings of EMNLP, pages 293--300, 2004.

[9]

D. Downey, S. Dumais, D. Liebling, and E. Horvitz. Understanding the relationship between searchers' queries and information goals. In Proceedings of CIKM, pages 449--458, 2008.

Digital Library

[10]

G. E. Dupret and B. Piwowarski. A user browsing model to predict search engine click data from past observations. In Proceedings of SIGIR, pages 331--338, 2008.

Digital Library

[11]

L. Finkelstein, E. Gabrilovich, Y. Matias, E. Rivlin, Z. Solan, G. Wolfman, and E. Ruppin. Placing Search in Context: The Concept Revisited. ACM Transactions on Information Systems, 20(1):116--131, 2002.

Digital Library

[12]

J. Guo, G. Xu, H. Li, and X. Cheng. A unified and discriminative model for query refinement. In Proceedings of SIGIR, pages 379--386, 2008.

Digital Library

[13]

D. Hawking. Challenges in enterprise search. In CRPIT '04: Proceedings of CRPIT, pages 15--24, 2004.

Digital Library

[14]

T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of KDD, pages 133--142, 2002.

Digital Library

[15]

T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay. Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Trans. Inf. Syst., 25(2):7, 2007.

Digital Library

[16]

R. Jones and K. L. Klinkner. Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs. In Proceedings of CIKM, pages 699--708, 2008.

Digital Library

[17]

R. Jones, B. Rey, O. Madani, and W. Greiner. Generating query substitutions. In Proceedings of WWW, pages 387--396, 2006.

Digital Library

[18]

G. Kumaran and J. Allan. A case for shorter queries, and helping user create them. In Proceedings of HLT, pages 220--227, 2006.

[19]

G. Kumaran and J. Allan. Effective and efficient user interaction for long queries. In Proceedings of SIGIR, pages 11--18, 2008.

Digital Library

[20]

T. Lau and E. Horvitz. Patterns of search: analyzing and modeling web query refinement. In Proceedings of UM, pages 119--128, 1999.

Digital Library

[21]

H. Ma, H. Yang, I. King, and M. R. Lyu. Learning latent semantic relations from clickthrough data for query suggestion. In Proceeding of CIKM, pages 709--718, 2008.

Digital Library

[22]

G. Marchionini and R. White. Find what you need, understand what you find. International Journal of Human-Computer Interaction, 23(3):205--237, 2007.

[23]

M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313--330, 1993.

Digital Library

[24]

Q. Mei and K. Church. Entropy of search logs: how hard is search? with personalization? with backoff? In Proceedings of WSDM, pages 45--54, 2008.

Digital Library

[25]

Q. Mei, H. Fang, and C. Zhai. A study of Poisson query generation model for information retrieval. In Proceedings of SIGIR, pages 319--326, 2007.

Digital Library

[26]

Q. Mei, D. Zhou, and K. Church. Query suggestion using hitting time. In Proceeding of CIKM, 2008.

Digital Library

[27]

D. Metzler and W. B. Croft. A Markov Random Field model for term dependencies. In Proceedings of SIGIR, pages 472--479, 2005.

Digital Library

[28]

G. Pass, A. Chowdhury, and C. Torgeson. A picture of search. In Proceedings of InfoScale. ACM Press, 2006.

Digital Library

[29]

N. Phan, P. Bailey, and R. Wilkinson. Understanding the relationship of information need specificity to search query length. In Proceedings of SIGIR, pages 709--710, 2007.

Digital Library

[30]

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

Digital Library

[31]

F. Radlinski, M. Kurup, and T. Joachims. How does clickthrough data reflect retrieval quality? In Proceedings of CIKM, pages 43--52, 2008.

Digital Library

[32]

C. Silverstein, H. Marais, M. Henzinger, and M. Moricz. Analysis of a very large web search engine query log. SIGIR Forum, 33(1):6--12, 1999.

Digital Library

[33]

B. Tan and F. Peng. Unsupervised query segmentation using generative language models and wikipedia. In Proceeding of WWW, pages 347--356, 2008.

Digital Library

[34]

X. Wang and C. Zhai. Mining term association patterns from search logs for effective query reformulation. In Proceeding of CIKM, pages 479--488, 2008.

Digital Library

[35]

X. Wei, F. Peng, and B. Dumoulin. Analyzing web text association to disambiguate abbreviation in queries. In Proceedings of SIGIR, pages 751--752, 2008.

Digital Library

[36]

X. Xue, J. Jeon, and W. B. Croft. Retrieval models for question and answer archives. In Proceedings of SIGIR, pages 475--482, 2008.

Digital Library

[37]

C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems, 22(2):179--214, 2004.

Digital Library

Cited By

Lafia SMillion AHemphill L(2024)Exploratory and directed search strategies at a social science data archiveIASSIST Quarterly10.29173/iq108748:1Online publication date: 28-Mar-2024
https://doi.org/10.29173/iq1087
Tavakoli LTrippas JZamani HScholer FSanderson M(2024)Online and Offline Evaluation in Search ClarificationACM Transactions on Information Systems10.1145/368178643:1(1-30)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3681786
Trippas JGallagher LMackenzie JSerra ESpezzano F(2024)Re-evaluating the Command-and-Control Paradigm in Conversational Search InteractionsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679588(2260-2270)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679588
Show More Cited By

Index Terms

Analysis of long queries in a large scale search log
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

WSCD '09: Proceedings of the 2009 workshop on Web Search Click Data

February 2009

95 pages

ISBN:9781605584348

DOI:10.1145/1507509

Copyright © 2009 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: 09 February 2009

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Division of Information and Intelligent Systems

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WSDM'09

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WSDM'09: Second ACM International Conference on Web Search and Web Data Mining

February 9, 2009

Barcelona, Spain

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Cited By

Lafia SMillion AHemphill L(2024)Exploratory and directed search strategies at a social science data archiveIASSIST Quarterly10.29173/iq108748:1Online publication date: 28-Mar-2024
https://doi.org/10.29173/iq1087
Tavakoli LTrippas JZamani HScholer FSanderson M(2024)Online and Offline Evaluation in Search ClarificationACM Transactions on Information Systems10.1145/368178643:1(1-30)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3681786
Trippas JGallagher LMackenzie JSerra ESpezzano F(2024)Re-evaluating the Command-and-Control Paradigm in Conversational Search InteractionsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679588(2260-2270)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679588
McGuire NMoshfeghi YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Prediction of the Realisation of an Information Need: An EEG StudyProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657981(2584-2588)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657981
Strzelecki AMiklosik A(2024)Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions dataAslib Journal of Information Management10.1108/AJIM-04-2023-0107Online publication date: 10-Jan-2024
https://doi.org/10.1108/AJIM-04-2023-0107
Michalkova DParra-Rodriguez MMoshfeghi YAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Information Need AwarenessProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531999(610-621)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531999
Bayatmakou FMohebi AAhmadi A(2021)An interactive query-based approach for summarizing scientific documentsInformation Discovery and Delivery10.1108/IDD-10-2020-012450:2(176-191)Online publication date: 14-Jun-2021
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Mansouri BSadegh Zahedi MCampos RFarhoodi M(2019)Exploring Video Game Searches on the WebCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3314999(1161-1170)Online publication date: 13-May-2019
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Mansouri BZanibbi ROard DDownie SZhu QHahn J(2019)Characterizing searches for mathematical conceptsProceedings of the 18th Joint Conference on Digital Libraries10.1109/JCDL.2019.00019(57-66)Online publication date: 2-Jun-2019
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Palanisamy RLiu Y(2019)Users’ Search Satisfaction in Search Engine OptimizationProceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018)10.1007/978-3-030-24643-3_124(1035-1045)Online publication date: 1-Aug-2019
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