Location- and Query-Aware Modeling of Browsing and Click Behavior in Sponsored Search
Article No.: 59, Pages 1 - 31
Abstract
An online advertisement’s clickthrough rate provides a fundamental measure of its quality, which is widely used in ad selection strategies. Unfortunately, ads placed in contexts where they are rarely viewed—or where users are unlikely to be interested in commercial results—may receive few clicks regardless of their quality. In this article, we model the variability of a user’s browsing behavior for the purpose of click analysis and prediction in sponsored search. Our model incorporates several important contextual factors that influence ad clickthrough rates, including the user’s query and ad placement on search engine result pages. We formally model these factors with respect to the list of ads displayed on a result page, the probability that the user will initiate browsing of this list, and the persistence of the user in browsing the list. We incorporate these factors into existing click models by augmenting them with appropriate query and location biases. Using expectation maximization, we learn the parameters of these augmented models from click signals recorded in the logs of a commercial search engine.
To evaluate the performance of the models and to compare them with state-of-the-art performance, we apply standard evaluation metrics, including log-likelihood and perplexity. Our evaluation results indicate that, through the incorporation of query and location biases, significant improvements can be achieved in predicting browsing and click behavior in sponsored search. In addition, we explore the extent to which these biases actually reflect varying behavioral patterns. Our observations confirm that correlations exist between the biases and user search behavior.
References
[1]
Z. Abrams and M. Schwarz. 2007. Ad auction design and user experience. Internet and Network Economics (2007), 529--534.
[2]
G. Aggarwal, J. Feldman, S. Muthukrishnan, and M. Pál. 2008. Sponsored search auctions with Markovian users. Internet and Network Economics (2008), 621--628.
[3]
Amazon Mechanical Turk. 2009. Homepage. Retrieved from http://www.mturk.com.
[4]
A. Ashkan. 2013. Characterizing User Search Intent and Behavior for Click Analysis in Sponsored Search. Ph.D. Dissertation. University of Waterloo, Waterloo, Ontario, Canada.
[5]
A. Ashkan and C. L. A. Clarke. 2009. Characterizing commercial intent. In Proceedings of the 18th ACM Conference on Information and Knowledge Management. 78--87.
[6]
A. Ashkan and C. L. A. Clarke. 2012. Modeling browsing behavior for click analysis in sponsored search. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2015--2019.
[7]
A. Ashkan and C.L.A. Clarke. 2013. Impact of query intent and search context on clickthrough behavior in sponsored search. Knowledge and Information Systems 34, 2 (2013), 425--452.
[8]
A. Broder, M. Ciaramita, M. Fontoura, E. Gabrilovich, V. Josifovski, D. Metzler, V. Murdock, and V. Plachouras. 2008. To swing or not to swing: Learning when (not) to advertise. In Proceedings of the 17th ACM Conference on Information and Knowledge Management.
[9]
O. Chapelle and Y. Zhang. 2009. A dynamic Bayesian network click model for web search ranking. In Proceedings of the 18th World Wide Web Conference. 1--10.
[10]
W. Chen, Z. Ji, S. Shen, and Q. Yang. 2011. A whole page click model to better interpret search engine click data. In Proceedings of the 21st Conference on Artificial Intelligence.
[11]
N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey. 2008. An experimental comparison of click position-bias models. In Proceedings of the first Web Search and Data Mining Conference. 87--94.
[12]
H. K. Dai, L. Zhao, Z. Nie, J. R. Wen, L. Wang, and Y. Li. 2006. Detecting online commercial intention (OCI). In Proceedings of the 15th International World Wide Web Conference. 829--837.
[13]
K. Debmbsczynski, W. Kotlowski, and D. Weiss. 2008. Predicting ads clickthrough rate with decision rules. In Proceedings of the WWW 2008 Workshop on Target and Ranking for Online Advertising.
[14]
A. P. Dempster, N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39, 1 (1977), 1--38.
[15]
G. E. Dupret and B. Piwowarski. 2008. A user browsing model to predict search engine click data from past observations. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 331--338.
[16]
B. Edelman, M. Ostrovsky, and M. Schwarz. 2005. Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords. Technical Report. National Bureau of Economic Research.
[17]
D. C. Fain and J. O. Pedersen. 2006. Sponsored search: A brief history. Bulletin of the American Society for Information Science and Technology 32, 2 (2006), 12--13.
[18]
T. K. Fan and C. H. Chang. 2010. Sentiment-oriented contextual advertising. Knowledge and Information Systems 23, 3 (2010), 321--344.
[19]
A. Ghose and S. Yang. 2008. An empirical analysis of sponsored search performance in search engine advertising. In Proceedings of the International Conference on Web Search and Web Data Mining. 241--250.
[20]
A. Ghosh and M. Mahdian. 2008. Externalities in online advertising. In Proceedings of the 17th International Conference on World Wide Web. 161--168.
[21]
T. Graepel, J. Q. Candela, T. Borchert, and R. Herbrich. 2010. Web-scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft’s Bing search engine. In Proceedings of the 27th International Conference on Machine Learning. 13--20.
[22]
F. Guo, C. Liu, and Y.M. Wang. 2009a. Efficient multiple-click models in web search. In Proceedings of the 2nd ACM International Conference on Web Search and Data Mining. ACM, 124--131.
[23]
F. Guo, C. Liu, A. Kannan, T. Minka, M. Taylor, Y. M. Wang, and C. Faloutsos. 2009b. Click chain model in Web search. In Proceedings of the 18th World Wide Web Conference. 11--20.
[24]
B. Hu, Y. Zhang, W. Chen, G. Wang, and Q. Yang. 2011. Characterizing search intent diversity into click models. In Proceedings of the 20th World Wide Web Conference. 17--26.
[25]
D. Hu, D. Shen, J. T. Sun, Q. Yang, and Z. Chen. 2009. Context-aware online commercial intention detection. Advances in Machine Learning (2009), 135--149.
[26]
B. J. Jansen. 2007. The comparative effectiveness of sponsored and nonsponsored links for web e-commerce queries. ACM Transactions on the Web 1, 1 (2007).
[27]
B. J. Jansen, A. Brown, and M. Resnick. 2007. Factors relating to the decision to click on a sponsored link. Decision Support Systems 44, 1 (2007), 46--59.
[28]
B. J. Jansen and M. Resnick. 2006. An examination of searcher’s perceptions of nonsponsored and sponsored links during ecommerce web searching. Journal of the American Society for Information Science and Technology 57, 14 (2006), 1949--1961.
[29]
T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. 2005. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th ACM SIGIR Conference on Research and Development in Information Retrieval. 154--161.
[30]
T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay. 2007. Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Transactions on Information Systems 25, 2 (2007).
[31]
C. Liu, F. Guo, and C. Faloutsos. 2009. BBM: Bayesian browsing model from petabyte-scale data. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 537--546.
[32]
MSR Data 2007. Beyond Search -- Semantic Computing and Internet Economics Program. (2007). http://research.microsoft.com/en-us/um/redmond/about/collaboration/awards/beyondsearchawards.aspx.
[33]
L. R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Speech Recognition 77, 2 (1989), 257--286.
[34]
M. Regelson and D. Fain. 2006. Predicting clickthrough rate using keyword clusters. In Proceedings of the 2nd Workshop on Sponsored Search Auctions.
[35]
M. Richardson. 2008. Learning about the world through long-term query logs. ACM Transactions on the Web 2, 4 (2008), 1--27.
[36]
M. Richardson, E. Dominowska, and R. Ragno. 2007. Predicting clicks: Estimating the clickthrough rate for new ads. In Proceedings of the 16th International World Wide Web Conference. 521--530.
[37]
C. Xiong, T. Wang, W. Ding, Y. Shen, and T.-Y. Liu. 2012. Relational click prediction for sponsored search. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 493--502.
[38]
J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, and Z. Chen. 2009. How much can behavioral targeting help online advertising?. In Proceedings of the 18th International Conference on World Wide Web. 261--270.
[39]
W. V. Zhang and R. Jones. 2007. Comparing click logs and editorial labels for training query rewriting. In Proceedings of the WWW Workshop on Query Log Analysis: Social and Technological Challenges.
[40]
Y. Zhang, W. Chen, D. Wang, and Q. Yang. 2011. User-click modeling for understanding and predicting search-behavior. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1388--1396.
[41]
Z. A. Zhu, W. Chen, T. Minka, C. Zhu, and Z. Chen. 2010. A novel click model and its applications to online advertising. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. 321--330.
Index Terms
- Location- and Query-Aware Modeling of Browsing and Click Behavior in Sponsored Search
Recommendations
Time-Aware Click Model
Click-through information is considered as a valuable source of users’ implicit relevance feedback for commercial search engines. As existing studies have shown that the search result position in a search engine result page (SERP) has a very strong ...
Modeling browsing behavior for click analysis in sponsored search
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementClickthrough rate provides a fundamental measure of advertising quality, which is widely used in ad selection strategies. However, ads placed in contexts where they are rarely viewed, or where users are unlikely to be interested in commercial results, ...
Impact of query intent and search context on clickthrough behavior in sponsored search
Implicit feedback techniques may be used for query intent detection, taking advantage of user behavior to understand their interests and preferences. In sponsored search, a primary concern is the user's interest in purchasing or utilizing a commercial ...
Comments
Information & Contributors
Information
Published In
January 2015
390 pages
Copyright © 2014 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]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 December 2014
Accepted: 01 September 2013
Revised: 01 June 2013
Received: 01 December 2012
Published in TIST Volume 5, Issue 4
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 212Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 26 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in