research-article

Characterizing commercial intent

Authors:

Charles L.A. ClarkeAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 67 - 76

https://doi.org/10.1145/1645953.1645965

Published: 02 November 2009 Publication History

Abstract

Understanding the intent underlying user's queries may help personalize search results and therefore improve user satisfaction. We develop a methodology for using the content of search engine result pages (SERPs) along with the information obtained from query strings to study characteristics of query intent, with a particular focus on sponsored search. This work represents an initial step towards the development and evaluation of an ontology for commercial search, considering queries that reference specific products, brands and retailers. The characteristics of query categories are studied with respect to aggregated user's clickthrough behavior on advertising links. We present a model for clickthrough behavior that considers the influence of such factors as the location of ads and the rank of ads, along with query category. We evaluate our work using a large corpus of clickthrough data obtained from a major commercial search engine. Our findings suggest that query based features, along with the content of SERPs, are effective in detecting query intent. The clickthrough behavior is found to be consistent with the classification for the general categories of query intent, while for product, brand and retailer categories, all is true to a lesser extent.

References

[1]

E. Agichtein, E. Brill, S. Dumais, and R. Ragno. Learning user interaction models for predicting Web search result preferences. Proceedings of the 29th ACM SIGIR conference on research and development in information retrieval, pages 3--10, 2006.

Digital Library

[2]

A. Ashkan, C. Clarke, E. Agichtein, and Q. Guo. Classifying and characterizing query intent. Proceedings of the 31st European Conference on Information Retrieval, pages 578--586, 2009.

Digital Library

[3]

A. Ashkan, C. Clarke, E. Agichtein, and Q. Guo. Estimating ad clickthrough rate through query intent analysis. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, 2009.

Digital Library

[4]

R. Baeza-Yates, L. Calderan-Benavides, and C. Gonzalez-Caro. The intention behind Web queries. Lecture Notes in Computer Science: SPIRE, 4209:98--109, 2006.

Digital Library

[5]

S. Beitzel, E. Jensen, O. Frieder, D. Grossman, D. Lewis, A. Chowdhury, and A. Kolcz. Automatic Web query classification using labeled and unlabeled training data. Proceedings of the 28th ACM SIGIR conference on Research and development in information retrieval, pages 581--582, 2005.

Digital Library

[6]

R. Briggs and N. Hollis. Advertising on the Web: Is there response before clickthrough. Journal of Advertising Research, 37(2), 1997.

[7]

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

Digital Library

[8]

A. Broder, M. Ciaramita, M. Fontoura, E. Gabrilovich, V. Josifovski, D. Metzler, V. Murdock, and V. Plachouras. To swing or not to swing: Learning when (not) to advertise. Proceedings of the 17th ACM conference on Information and knowledge management, 2008.

Digital Library

[9]

A. Broder, M. Fontoura, E. Gabrilovich, A. Joshi, V. Josifovski, and T. Zhang. Robust classification of rare queries using Web knowledge. Proceedings of the 30th ACM SIGIR conference on Research and development in information retrieval, pages 231--238, 2007.

Digital Library

[10]

J. Cohen. A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1):37, 1960.

[11]

T. Cover and J. Thomas. Elements of information theory. Wiley-Interscience, 2006.

Digital Library

[12]

H. Dai, L. Zhao, Z. Nie, J. Wen, L. Wang, and Y. Li. Detecting Online Commercial Intention (OCI). Proceedings of the 15th International Conference on World Wide Web, pages 829--837, 2006.

Digital Library

[13]

K. Debmbsczynski, W. Kotlowski, and D. Weiss. Predicting ads clickthrough rate with decision rules. Workshop on Target and Ranking for Online Advertising, WWW 2008.

Digital Library

[14]

J. Fleiss and J. Cohen. The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and Psychological Measurement, 33(3):613, 1973.

[15]

A. Ghose and S. Yang. An empirical analysis of sponsored search performance in search engine advertising. Proceedings of the International Conference on Web Search and Web Data Mining, pages 241--250, 2008.

Digital Library

[16]

M. Grant and S. Boyd. CVX: Matlab software for disciplined convex programming, 2009. URL = http://stanford.edu/~boyd/cvx.

[17]

B. Jansen. The comparative e ectiveness of sponsored and nonsponsored links for Web e-commerce queries. ACM Transactions on the Web, 1(1), 2007.

Digital Library

[18]

T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. Proceedings of the 28th ACM SIGIR conference on Research and development in information retrieval, pages 154--161, 2005.

Digital Library

[19]

A. Lacerda, M. Cristo, M. Goncalves, W. Fan, N. Ziviani, and B. Ribeiro-Neto. Learning to advertise. Proceedings of the 29th ACM SIGIR conference on Research and development in information retrieval, pages 549--556, 2006.

Digital Library

[20]

J. Landis and G. Koch. The measurement of observer agreement for categorical data. Biometrics, 33:159--174, 1977.

[21]

U. Lee, Z. Liu, and J. Cho. Automatic identification of user goals in Web search. Proceedings of the 14th International Conference on World Wide Web, pages 391--400, 2005.

Digital Library

[22]

X. Li, Y. Wang, and A. Acero. Learning query intent from regularized click graphs. Proceedings of the 31st ACM SIGIR conference on Research and development in information retrieval, pages 339--346, 2008.

Digital Library

[23]

D. Nettleton, L. Calderan-Benavides, and R. Baeza-Yates. Analysis of Web search engine query session and clicked documents. Lecture Notes in Computer Science: Advances in Web Mining and Web Usage Analysis, 4811:207--226, 2007.

Digital Library

[24]

B. Poblete and R. Baeza-Yates. Query-sets: using implicit feedback and query patterns to organize Web documents. Proceedings of the 17th international World Wide Web Conference, pages 41--50, 2008.

Digital Library

[25]

M. Richardson. Learning about the world through long-term query logs. ACM Transactions on the Web, 2008.

Digital Library

[26]

M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the clickthrough rate for new ads. Proceedings of the 16th International Conference on World Wide Web, pages 521--530, 2007.

Digital Library

[27]

D. Rose and D. Levinson. Understanding user goals in Web search. Proceedings of the 13th International Conference on World Wide Web, pages 13--19, 2004.

Digital Library

[28]

T. Joachims, SVMlight support vector machine, 2008. URL = http://svmlight.joachims.org.

[29]

B. Tan and F. Peng. Unsupervised query segmentation using generative language models and wikipedia. Proceedings of the 17th international World Wide Web Conference, pages 347--356, 2008.

Digital Library

[30]

J. Teevan, S. Dumais, and D. Liebling. To personalize or not to personalize: Modeling queries with variation in user intent. Proceedings of the 31st ACM SIGIR conference on research and development in information retrieval, pages 163--170, 2008.

Digital Library

Cited By

Mayhook Z(2024)Using keyword analysis for market validation researchJournal of Business & Finance Librarianship10.1080/08963568.2024.241147430:1(1-17)Online publication date: 7-Oct-2024
https://doi.org/10.1080/08963568.2024.2411474
Kawada YYamamoto TOhshima HYanagida YKato MFujita S(2023)Analyzing Web Search Queries of Before and After Purchase on e-Commerce SiteLeveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration10.1007/978-981-99-8085-7_16(181-187)Online publication date: 30-Nov-2023
https://doi.org/10.1007/978-981-99-8085-7_16
Shani CLibov ATolmach SLewin-Eytan LMaarek YShahaf D(2022)“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational AgentsExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519870(1-7)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3491101.3519870
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Index Terms

Characterizing commercial intent
1. Information systems
  1. World Wide Web

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cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 ACM.

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Published: 02 November 2009

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November 2 - 6, 2009

Hong Kong, China

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

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Cited By

Mayhook Z(2024)Using keyword analysis for market validation researchJournal of Business & Finance Librarianship10.1080/08963568.2024.241147430:1(1-17)Online publication date: 7-Oct-2024
https://doi.org/10.1080/08963568.2024.2411474
Kawada YYamamoto TOhshima HYanagida YKato MFujita S(2023)Analyzing Web Search Queries of Before and After Purchase on e-Commerce SiteLeveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration10.1007/978-981-99-8085-7_16(181-187)Online publication date: 30-Nov-2023
https://doi.org/10.1007/978-981-99-8085-7_16
Shani CLibov ATolmach SLewin-Eytan LMaarek YShahaf D(2022)“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational AgentsExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519870(1-7)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3491101.3519870
Balog KBalog K(2018)Understanding Information NeedsEntity-Oriented Search10.1007/978-3-319-93935-3_7(225-267)Online publication date: 3-Oct-2018
https://doi.org/10.1007/978-3-319-93935-3_7
Bauer SRadlinski FWhite RBourdeau JHendler JNkambou RHorrocks IZhao B(2016)Where Can I Buy a Boulder?Proceedings of the 25th International Conference on World Wide Web10.1145/2872427.2882998(1225-1235)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.1145/2872427.2882998
Alchalabi AElsharnoby MKhawaldeh S(2016)Rightscope: Detecting search campaingns positive and negative queries2016 International Conference on Machine Learning and Cybernetics (ICMLC)10.1109/ICMLC.2016.7860916(290-295)Online publication date: Jul-2016
https://doi.org/10.1109/ICMLC.2016.7860916
Yin DMei SCao BSun JDavison BCarterette BDiaz FCastillo CMetzler D(2014)Exploiting contextual factors for click modeling in sponsored searchProceedings of the 7th ACM international conference on Web search and data mining10.1145/2556195.2556237(113-122)Online publication date: 24-Feb-2014
https://dl.acm.org/doi/10.1145/2556195.2556237
Ashkan AClarke C(2014)Location- and Query-Aware Modeling of Browsing and Click Behavior in Sponsored SearchACM Transactions on Intelligent Systems and Technology10.1145/25343985:4(1-31)Online publication date: 29-Dec-2014
https://dl.acm.org/doi/10.1145/2534398
Ashkan AClarke C(2013)Impact of query intent and search context on clickthrough behavior in sponsored searchKnowledge and Information Systems10.1007/s10115-012-0485-x34:2(425-452)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s10115-012-0485-x
Ashkan AClarke CChen XLebanon GWang HZaki M(2012)Modeling browsing behavior for click analysis in sponsored searchProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398563(2015-2019)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398563
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