Abstract
Online advertising has become lucrative for businesses to reach millions of customers. Revenue model for online advertising is different from conventional practice, as advertisers get charged only when users click on their advertisement; this method is referred as “pay-per-click”. Several studies have been carried using multi-criteria regression and kernel prediction algorithms to analyse click patterns. User profile and purchase history helps to identify the ad content to be displayed and the order in which they are displayed. Click-through rating method increases the probability of user watching and clicking on each ad. In the current study, we present a novel approach to improve the accuracy of the frequently clicked advertisements by using firefly algorithm. We have used KDD Cup 2012 datasets collected from popular websites selling US and UK products. The results of our study show 0.99 accuracy confirming our approach to be better than existing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
B.G. Babu, M. Kannan, Lightning bugs. Resonance 7(9), 49–55 (2002)
H. Fraga, Firefly luminescence: a historical perspective and recent developments. Photochem. Photobiol. Sci. 7(2), 146–158 (2008)
Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization, in Proceedings of the 1999 congress on Evolutionary computation, 1999. CEC 99, vol. 3 (IEEE, 1999), pp. 1945–1950
H.P. Schwefel, Numerical Optimization of Computer Models (Wiley, NY, 1981)
M. Richardson, E. Dominowska, R. Ragno, Predicting clicks: estimating the click-through rate for new ads, in Proceedings of the 16th International Conference on World Wide Web (2007), pp. 521–530
C. Deepayan, D. Agarwal, V. Josifovski, Contextual advertising by combining relevance with click feedback, in Proceedings of the 17th International Conference the on World Wide Web (2008), pp. 417–426
L. Yan, L. Wu-Jun, Coupled group lasso for web-scale CTR prediction in display advertising, in ICML’14 Proceedings of the 31st International Conference on Machine Learning, vol. 32 (2014), pp. II-802–810
Z. Gao, Q. Gao, Ad-centric model discovery for predicting ad’s click-through rate, in The 4th International Conference on Ambient Systems, Networks and Technologies (ANT, 2013)
S. Kim, T. Qin, H. Yu, Advertiser-centric approach to understanding behavior in sponsored search, in The Proceedings of the 20th ACM International Conference on Information and Knowledge Management
A.H. Gandomi, X.S. Yang, A.H. Alavi, Mixed variable structural optimization using firefly algorithm. Comput. Struct. 89(23–24), 2325–2336 (2011)
X.S. Yang, Nature-Inspired Metaheuristic Algorithms (Luniver Press, 2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Madhu Sudana Rao, N., Eranki, K.L.N., Harika, D.L., Kavya Sree, H., Sai Prudhvi, M.M., Rajasekar Reddy, M. (2020). Optimization of Click-Through Rate Prediction of an Advertisement. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-15-1286-5_50
Download citation
DOI: https://doi.org/10.1007/978-981-15-1286-5_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1285-8
Online ISBN: 978-981-15-1286-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)