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research-article

Click-through prediction for news queries

Authors:

Arnd Christian König,

Qiang WuAuthors Info & Claims

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

Pages 347 - 354

https://doi.org/10.1145/1571941.1572002

Published: 19 July 2009 Publication History

Abstract

A growing trend in commercial search engines is the display of specialized content such as news, products, etc. interleaved with web search results. Ideally, this content should be displayed only when it is highly relevant to the search query, as it competes for space with "regular" results and advertisements. One measure of the relevance to the search query is the click-through rate the specialized content achieves when displayed; hence, if we can predict this click-through rate accurately, we can use this as the basis for selecting when to show specialized content. In this paper, we consider the problem of estimating the click-through rate for dedicated news search results. For queries for which news results have been displayed repeatedly before, the click-through rate can be tracked online; however, the key challenge for which previously unseen queries to display news results remains. In this paper we propose a supervised model that offers accurate prediction of news click-through rates and satisfies the requirement of adapting quickly to emerging news events.

References

[1]

D. Agarwal, B. Chen, P. Elango, R. Ramakrishnan, N. Motgi, S. Roy, and J. Zachariah. Online Models for Content Optimization. In NIPS, 2008.

[2]

E. Agichtein, E. Brill, and S. Dumais. Improving Web Search Ranking by Incorporating User Behavior Information. In ACM SIGIR, pages 19--26, 2006.

Digital Library

[3]

J. Allan, R. Papka, and V. Lavrenko. On-line New Event Detection and Tracking. In ACM SIGIR, 1998.

Digital Library

[4]

C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to Rank using Gradient Descent. In ICML, 2005.

Digital Library

[5]

J. Callan. Distributed Information Retrieval. In Advances in Information Retrieval, pages 127--150, 2000.

[6]

D. Carmel, E. Yom-Tov, A. Darlow, and D. Pelleg. What makes a Query Difficult? In ACM SIGIR, 2006.

Digital Library

[7]

D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual Advertising by Combining Relevance with Click Feedback. In WWW Conference, 2008.

Digital Library

[8]

G. M. D. Corso, A. Gull´1, and F. Romani. Ranking a Stream of News. In Proceedings of the 14th international conference on World Wide Web, pages 97--106, 2005.

Digital Library

[9]

F. Diaz. Integration of News Content into Web Results. In WSDM Conference, 2009.

Digital Library

[10]

J. Friedman. Greedy Function Approximation: a Gradient Boosting Machine. Annals of Statistics, 29(5), 2001.

[11]

T. Jayram, S. Khot, R. Kumar, and Y. Rabani. Cell-Probe Lower Bounds for the Partial Match Problem. In STOC, 2003.

Digital Library

[12]

K. S. Jones, S. Walker, and S. E. Robertson. A probabilistic model of information retrieval: Development and comparative experiments. Information Processing and Management, 36(6), 2000.

Digital Library

[13]

A. C. König, K. Church, and M. Markov. A Data Struture for Sponsored Search. In IEEE ICDE Conference, 2009.

Digital Library

[14]

P. Li and K. W. Church. Using Sketches to Estimate Two-way and Multi-way Associations. Computational Linguistics, 33, 2007.

Digital Library

[15]

X. Li, Y.-Y. Wang, and A. Acero. Learning Query Intent from Regularized Click Graphs. In In Proc. ACM of SIGIR, 2008.

Digital Library

[16]

J. Lin. Divergence Measures based on the Shannon Entropy. IEEE Trans. on Information Theory, 37(1), 1991.

[17]

C. Manning and H. Sch¨utze. Foundations of Statistical Natural Language Processing. MIT Press, 1999.

Digital Library

[18]

A. Maykov and M. Hurst. Social Streams Blog Crawler. In M3SN Workshop at IEEE ICDE Conference, 2009.

Digital Library

[19]

G. Navarro and V. M¨akinen. Compressed full-text indexes. ACM Comput. Surv., 39(1), 2007.

Digital Library

[20]

F. Radlinski and T. Joachims. Active Exploration for Learning Rankings from Clickthrough Data. In ACM SIKDD, 2007.

Digital Library

[21]

M. Regelson and D. Fain. Predicting Click-through Rate using Keyword Clusters. In 2nd Workshop on Sponsored Search Auctions, 2006.

[22]

M. Richardson, E. Dominowska, and R. Ragno. Predicting Clicks: Estimating the Click-Through Rate for New Ads. In WWW Conference, pages 521--529, 2007.

Digital Library

[23]

Technorati. State of the Live Web. http://technorati.com/weblog/2007/04/328.html, April 2007.

[24]

Technorati. State of the Blogosphere 2008. http://technorati.com/blogging/state-of-the-blogosphere/, 2008.

[25]

M. West and J. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, 1997.

Digital Library

[26]

Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Ranking, Boosting, and Model Adaptation. Technical report, Microsoft Research, 2008

Cited By

Nayak AGarg MDuvvuru Muni RChen HDuh WHuang HKato MMothe JPoblete B(2023)News Popularity Beyond the Click-Through-Rate for Personalized RecommendationsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591741(1396-1405)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591741
Dambanemuya HDiakopoulos N(2021)Auditing the Information Quality of News-Related Queries on the Alexa Voice AssistantProceedings of the ACM on Human-Computer Interaction10.1145/34491575:CSCW1(1-21)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449157
M.K Musa ND. Nyakoe GOdhiambo K(2020)Optimisation of Online Newspaper Headline Length with CharactersEAST AFRICAN JOURNAL OF EDUCATION AND SOCIAL SCIENCES10.46606/eajess2020v01i02.00301:2(150-157)Online publication date: 7-Sep-2020
https://doi.org/10.46606/eajess2020v01i02.0030
Show More Cited By

Index Terms

Click-through prediction for news queries
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

July 2009

896 pages

ISBN:9781605584836

DOI:10.1145/1571941

General Chairs:
James Allan
University of Massachusetts Amherst, USA
,
Javed Aslam
Northeastern University, USA
,
Program Chairs:
Mark Sanderson
University of Sheffield, UK
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Justin Zobel
University of Melbourne, Australia

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: 19 July 2009

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SIGIR '09: The 32nd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2009

MA, Boston, USA

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

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Cited By

Nayak AGarg MDuvvuru Muni RChen HDuh WHuang HKato MMothe JPoblete B(2023)News Popularity Beyond the Click-Through-Rate for Personalized RecommendationsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591741(1396-1405)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591741
Dambanemuya HDiakopoulos N(2021)Auditing the Information Quality of News-Related Queries on the Alexa Voice AssistantProceedings of the ACM on Human-Computer Interaction10.1145/34491575:CSCW1(1-21)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449157
M.K Musa ND. Nyakoe GOdhiambo K(2020)Optimisation of Online Newspaper Headline Length with CharactersEAST AFRICAN JOURNAL OF EDUCATION AND SOCIAL SCIENCES10.46606/eajess2020v01i02.00301:2(150-157)Online publication date: 7-Sep-2020
https://doi.org/10.46606/eajess2020v01i02.0030
Lagerwerf LGovaert C(2020)Raising Clickworthiness: Effects of Foregrounding News Values in Online Newspaper HeadlinesNews Values from an Audience Perspective10.1007/978-3-030-45046-5_6(95-119)Online publication date: 12-Dec-2020
https://doi.org/10.1007/978-3-030-45046-5_6
Papakonstantinou MDelis A(2019)PolyRecs: Improving Page–View Rates Using Real-Time Data AnalysisReal-Time Business Intelligence and Analytics10.1007/978-3-030-24124-7_7(95-112)Online publication date: 11-Oct-2019
https://doi.org/10.1007/978-3-030-24124-7_7
Cucchiarelli AMorbidoni CStilo GVelardi PHaddad HWainwright RChbeir R(2018)What to write and whyProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167274(1321-1330)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167274
Zhang XHan SLu W(2018)Automatic prediction of news intent for search queriesThe Electronic Library10.1108/EL-06-2017-013436:5(938-958)Online publication date: Oct-2018
https://doi.org/10.1108/EL-06-2017-0134
Gupta RPal S(2018)Click-Through Rate Estimation Using CHAID Classification Tree ModelAdvances in Analytics and Applications10.1007/978-981-13-1208-3_5(45-58)Online publication date: 8-Sep-2018
https://doi.org/10.1007/978-981-13-1208-3_5
Deng YShen YJin H(2017)Disguise adversarial networks for click-through rate predictionProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172108(1589-1595)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3172077.3172108
Arguello J(2017)Aggregated SearchFoundations and Trends in Information Retrieval10.1561/150000005210:5(365-502)Online publication date: 6-Mar-2017
https://dl.acm.org/doi/10.1561/1500000052
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