CFP last date
21 October 2024
Reseach Article

Click Through Rate Prediction for Display Advertisement

by Avila Clemenshia P., Vijaya M. S.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 1
Year of Publication: 2016
Authors: Avila Clemenshia P., Vijaya M. S.
10.5120/ijca2016908332

Avila Clemenshia P., Vijaya M. S. . Click Through Rate Prediction for Display Advertisement. International Journal of Computer Applications. 136, 1 ( February 2016), 18-24. DOI=10.5120/ijca2016908332

@article{ 10.5120/ijca2016908332,
author = { Avila Clemenshia P., Vijaya M. S. },
title = { Click Through Rate Prediction for Display Advertisement },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 1 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number1/24117-2016908332/ },
doi = { 10.5120/ijca2016908332 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:35:51.636760+05:30
%A Avila Clemenshia P.
%A Vijaya M. S.
%T Click Through Rate Prediction for Display Advertisement
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 1
%P 18-24
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computational Advertising is the currently emerging multidimensional statistical modeling sub-discipline in digital advertising industry. Web pages visited per user every day is considerably increasing, resulting in an enormous access to display advertisements (ads). The rate at which the ad is clicked by users is termed as the Click Through Rate (CTR) of an advertisement. This metric facilitates the measurement of the effectiveness of an advertisement. The placement of ads in appropriate location leads to the rise in the CTR value that influences the growth of customer access to advertisement resulting in increased profit rate for the ad exchange, publishers and advertisers. Thus it is imperative to predict the CTR metric in order to formulate an efficient ad placement strategy. This paper proposes a predictive model that generates the click through rate based on different dimensions of ad placement for display advertisements using statistical machine learning regression techniques such as multivariate linear regression (LR), poisson regression (PR) and support vector regression(SVR). The experiment result reports that SVR based click model outperforms in predicting CTR through hyperparameter optimization.

References
  1. Abirami, R and Vijaya, M. S. 2012. Stock Price Prediction using Support vector regression CCIS 269, pp 588 – 597@ Springer Verlag Berlin Heidelberg 2012.
  2. Agarwal, D., Agrawal, R., Khanna, R.. and Kota, N. 2010. Estimating rates of rare events with multiple hierarchies through scalable log-linear models. In Proceedings of the ACM SIGKDD Knowledge Discovery and Data Mining, pages 213–222.
  3. Agarwal, D., Agrawal, R., Khanna, R.. and Kota, N. 2010. Estimating rates of rare events with multiple hierarchies through scalable log-linear models. In Proceedings of the ACM SIGKDD Knowledge Discovery and Data Mining, pages 213–222.
  4. Agarwal, D., Chen, B.-C. and Elango, P. 2009. Spatio-temporal models for estimating click-through rate. In Proceedings of the International World WideWeb Conference, pages 21–30, New York.
  5. Dawei Yin, Shike Mei, Bin Cao, Jian-Tao Sun Brian and Davison, D. 2014. Exploiting Contextual Factors for Click Modeling in Sponsored Search. ACM 978-1-4503-2351-2/14/02.
  6. Deepayan Chakrabarti, Deepak Agarwal and Vanja Josifovski. 2014. Contextual Advertising by Combining Relevance with Click Feedback. Proceedings of the 31 st International Conference on Machine Learning, Beijing, China, 2014. JMLR: W&CP volume 32, 2008, Beijing, China. ACM 978-1-60558-085-2/08/04.
  7. Fang Wanga*, Warawut Suphamitmongkola and Bo Wanga. 2013. Advertisement Click-Through Rate Prediction using Multiple Criteria Linear Programming Regression Model. Elsevier B.V, Procedia Computer Science 17 p. 803 – 811.
  8. Haibin Cheng and Erick Cantú-Paz. 2010. Personalized Click Prediction in Sponsored Search. ACM 978-1-60558-889-6/10/02.
  9. Kushal Dave and Vasudeva Varma. 2010. Predicting the Click-Through Rate for Rare/New Ads. Report No: IIIT/TR/2010/15, Centre for Search and Information Extraction Lab International Institute of Information Technology.
  10. Menon, A. K., Chitrapura, K.-P., Garg, S., Agarwal, D. and Kota, N. 2011. Response prediction using collaborative filtering with hierarchies and side-information. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 141–149.
  11. Olivier Chapelle and Ya Zhang, 2009. A dynamic bayesian network click model for web search ranking. WWW ’09: Proceedings of the 18th international conference on World wide web, pages 1–10, New York, NY, USA. ACM.
  12. Richardson, M., Ewa Dominowska and Robert Ragno. 2007. Predicting clicks: Estimating the click-through rate for new ad. In Proceedings of the International World Wide Web Conference, pages 521–530.
  13. Soman, K. P., Loganathan, R. and Ajay, V., Machine Learning with SVM and other kernel methods. Prentice Hall, 2009.
  14. Thore Graepel, Joaquin Quinonero Candela, Thomas Borchert and Ralf Herbrich. 2010. Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine. Proceedings of the 27th International Conference on Machine Learning, ICML 2010.
  15. Ling Yan, Wu-Jun Li, Gui-Rong Xue and Dingyi Han. 2014. Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising. Proceedings of the 31 st International Conference on Machine Learning, Beijing, China, JMLR: W&CP volume 32.
  16. Zhipeng Fang, Kun Yue, Jixian Zhang, Dehai Zhang and Weiyi Liu. 2014. Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network. Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 818203, 9 pages.
Index Terms

Computer Science
Information Sciences

Keywords

ad campaign metrics click through rate display advertisements prediction.