research-article

Click patterns: an empirical representation of complex query intents

Authors:

ChengXiang ZhaiAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 1035 - 1044

https://doi.org/10.1145/2396761.2398400

Published: 29 October 2012 Publication History

Abstract

Understanding users' search intents is critical component of modern search engines. A key limitation made by most query log analyses is the assumption that each clicked web result represents one unique intent. However, there are many search tasks, such as comparison shopping or in-depth research, where a user's intent is to explore many documents. In these cases, the assumption of a one-to-one correspondence between clicked documents and user intent breaks down.

To capture and understand such behaviors, we propose the use of click patterns. Click patterns capture the relationship among clicks on search results by treating the set of clicks made by a user as a single unit. We aggregate click patterns together using a hierarchical clustering algorithm to discover the common click patterns. By using click patterns as an empirical representation of user intent, we are able to create a rich representation of mixtures of multiple navigational and informational intents. We analyze real search logs and demonstrate that such complex mixtures of intents do occur in the wild and can be identified using click patterns.

We further demonstrate the usefulness of click patterns by integrating them into a measure of query ambiguity and into a query recommendation task. We show that calculating query ambiguity as the entropy over the distribution of click patterns provides a measure of ambiguity with improved discriminative power, consistency and temporal stability as compared to previous measures of ambiguity. We explore the use of click pattern similarity and click pattern entropy in generating query recommendations and show promising results.

References

[1]

D. N. Aurelio and R. R. Mourant. The effects of web search engine query ambiguity and results sorting method on user performance and preference. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 46(14):1271--1275, 2002.

[2]

R. Baeza-yates, C. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In In International Workshop on Clustering Information over the Web (ClustWeb, in conjunction with EDBT), Creete, pages 588--596. Springer, 2004.

Digital Library

[3]

S. Bhatia, D. Majumdar, and P. Mitra. Query suggestions in the absence of query logs. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, SIGIR '11, pages 795--804, New York, NY, USA, 2011. ACM.

Digital Library

[4]

N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey. An experimental comparison of click position-bias models. In Proceedings of the international conference on Web search and web data mining, WSDM '08, pages 87--94, New York, NY, USA, 2008. ACM.

Digital Library

[5]

S. Cronen-Townsend and W. B. Croft. Quantifying query ambiguity. In Proceedings of the second international conference on Human Language Technology Research, HLT '02, pages 104--109, San Francisco, CA, USA, 2002. Morgan Kaufmann Publishers Inc.

Digital Library

[6]

K. Darwish and D. W. Oard. Probabilistic structured query methods. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, SIGIR '03, pages 338--344, New York, NY, USA, 2003. ACM.

Digital Library

[7]

H. Daumé, III and E. Brill. Web search intent induction via automatic query reformulation. In Proceedings of HLT-NAACL 2004: Short Papers, HLT-NAACL-Short '04, pages 49--52, Stroudsburg, PA, USA, 2004. Association for Computational Linguistics.

Digital Library

[8]

G. Dupret and B. Piwowarski. A user behavior model for average precision and its generalization to graded judgments. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '10, pages 531--538, New York, NY, USA, 2010. ACM.

Digital Library

[9]

J. Gonzalo, F. Verdejo, I. Chugur, and J. Cigarrin. Indexing with wordnet synsets can improve text retrieval. In Proceedings of CORR, pages 38--44, 1998.

[10]

J. Hu, G. Wang, F. Lochovsky, J.-t. Sun, and Z. Chen. Understanding user's query intent with wikipedia. In Proceedings of the 18th international conference on World wide web, WWW '09, pages 471--480, New York, NY, USA, 2009. ACM.

Digital Library

[11]

R. Krovetz and W. B. Croft. Lexical ambiguity and information retrieval. ACM Trans. Inf. Syst., 10:115--141, April 1992.

Digital Library

[12]

X. Li, Y. yi Wang, and A. Acero. Learning query intent from regularized click graphs. In SIGIR 2008, pages 339--346. ACM, 2008.

Digital Library

[13]

Q. Mei and K. Church. Entropy of search logs: how hard is search? with personalization? with backoff? In Proceedings of the international conference on Web search and web data mining, WSDM '08, pages 45--54, New York, NY, USA, 2008. ACM.

Digital Library

[14]

M. Sanderson. Ambiguous queries: test collections need more sense. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pages 499--506, New York, NY, USA, 2008. ACM.

Digital Library

[15]

M. Sanderson and C. J. Van Rijsbergen. The impact on retrieval effectiveness of skewed frequency distributions. ACM Trans. Inf. Syst., 17:440--465, October 1999.

Digital Library

[16]

C. Shannon. A mathematical theory of communication. Bell System Technical Journal, 27:379--423,623--656, 1948.

[17]

R. Song, Z. Luo, J.-R. Wen, Y. Yu, and H.-W. Hon. Identifying ambiguous queries in web search. In Proceedings of the 16th international conference on World Wide Web, WWW '07, pages 1169--1170, New York, NY, USA, 2007. ACM.

Digital Library

[18]

C. Stokoe, M. P. Oakes, and J. Tait. Word sense disambiguation in information retrieval revisited. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, SIGIR '03, pages 159--166, New York, NY, USA, 2003. ACM.

Digital Library

[19]

J. Teevan, S. T. Dumais, and D. J. Liebling. To personalize or not to personalize: modeling queries with variation in user intent. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pages 163--170, New York, NY, USA, 2008. ACM.

Digital Library

[20]

O. Uzuner, B. Katz, and D. Yuret. Word sense disambiguation for information retrieval. In Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence, AAAI '99/IAAI '99, pages 985--, Menlo Park, CA, USA, 1999. American Association for Artificial Intelligence.

Digital Library

[21]

Y. Wang and E. Agichtein. Query ambiguity revisited: clickthrough measures for distinguishing informational and ambiguous queries. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT '10, pages 361--364, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.

Digital Library

[22]

S. F. Weiss. Learning to disambiguate. Information Storage and Retrieval, 9(1):33--41, 1973.

[23]

E. Yilmaz, M. Shokouhi, N. Craswell, and S. Robertson. Expected browsing utility for web search evaluation. In Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10, pages 1561--1564, New York, NY, USA, 2010. ACM.

Digital Library

[24]

Z.-J. Zha, L. Yang, T. Mei, M. Wang, and Z. Wang. Visual query suggestion. In Proceedings of the 17th ACM international conference on Multimedia, MM '09, pages 15--24, New York, NY, USA, 2009. ACM.

Digital Library

Cited By

Floris FConte MRabellino SRoman FSacchet M(2024)Investigating Engagement and Performance in Online Mathematics Courses Using Clustering TechniquesSmart Learning Environments in the Post Pandemic Era10.1007/978-3-031-54207-7_5(71-86)Online publication date: 26-Mar-2024
https://doi.org/10.1007/978-3-031-54207-7_5
Rha EMitsui MBelkin NShah C(2016)Exploring the relationships between search intentions and query reformulationsProceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology10.5555/3017447.3017495(1-9)Online publication date: 14-Oct-2016
https://dl.acm.org/doi/10.5555/3017447.3017495
Yano YTagami YTajima AMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Quantifying Query Ambiguity with Topic DistributionsProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983863(1877-1880)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983863
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Index Terms

Click patterns: an empirical representation of complex query intents
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

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Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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

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Cited By

Floris FConte MRabellino SRoman FSacchet M(2024)Investigating Engagement and Performance in Online Mathematics Courses Using Clustering TechniquesSmart Learning Environments in the Post Pandemic Era10.1007/978-3-031-54207-7_5(71-86)Online publication date: 26-Mar-2024
https://doi.org/10.1007/978-3-031-54207-7_5
Rha EMitsui MBelkin NShah C(2016)Exploring the relationships between search intentions and query reformulationsProceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology10.5555/3017447.3017495(1-9)Online publication date: 14-Oct-2016
https://dl.acm.org/doi/10.5555/3017447.3017495
Yano YTagami YTajima AMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Quantifying Query Ambiguity with Topic DistributionsProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983863(1877-1880)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983863
Kravi EGuy IMejer ACarmel DMaarek YPelleg DTsur GMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)One Query, Many ClicksProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983856(1423-1432)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983856
Song HXu YMin HWu QWei WWeng JHan XYang QShi JGu JMiao CToyoaki N(2016)Individual Judgments Versus ConsensusACM Transactions on the Web10.1145/283412210:1(1-21)Online publication date: 9-Jan-2016
https://dl.acm.org/doi/10.1145/2834122
Jamil H(2014)Mapping abstract queries to big data web resources for on-the-fly data integration and information retrieval2014 IEEE 30th International Conference on Data Engineering Workshops10.1109/ICDEW.2014.6818304(62-67)Online publication date: Mar-2014
https://doi.org/10.1109/ICDEW.2014.6818304
Han XZhou WJiang XSong HZhong MNishida T(2013)Utilizing URLs Position to Estimate Intrinsic Query-URL Relevance2013 IEEE 13th International Conference on Data Mining10.1109/ICDM.2013.20(251-260)Online publication date: Dec-2013
https://doi.org/10.1109/ICDM.2013.20

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