Abstract
Online advertising is an industry with the potential for maximum revenue extraction. Displaying the ad which is more likely to be clicked plays a crucial role in generating maximum revenue. A high click through rate (CTR) is an indication that the user finds the ad useful and relevant. For suitable placement of ads online and rich user experience, determining CTR has become imperative. Accurate estimation of CTR helps in placement of advertisements in relevant locations which would result in more profits and return of investment for the advertisers and publishers. This paper presents the application of a soft clustering method namely fuzzy c-means (FCM) clustering for determining if a particular ad would be clicked by the user or not. This is done by classifying the ads in the dataset into broad clusters depending on whether they were actually clicked or not. This way the kind of advertisements that the user is interested in can be found out and subsequently more advertisements of the same kind can be recommended to him, thereby increasing the CTR of the displayed ads. Experimental results show that FCM outperforms k-means clustering (KMC) in determining CTR.
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
References
Avila Clemenshia, P., Vijaya, M.S.: Click through rate prediction for display advertisement. Int. J. Comput. Appl. 975, 8887 (2016)
Graepel, T., Candela, J., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In: IJCA (2010)
Hillard, D., Schroedl, S., Manavoglu, E., Raghavan, H., Leggetter, C.: Im-proving ad relevance in sponsored search. In: ACM (2010)
Kondakindi, G., Rana, S., Rajkumar, A., Ponnekanti, S.K., Parakh, V.: A logistic regression approach to ad click prediction (2014)
Shi, L., Li, B.: Predict the click through rate and average cost per click for key-words using machine learning methodologies. In: IEOM (2016)
Wang., C.J., Chen, H.H.: Learning user behaviors for advertisements click predictions. In: ACM (2011)
Chakrabarti, D., Agarwal, D., Josifovski, V.: Contextual advertising by combining relevance with click feedback. In: WWW (2008)
Cheng, H., Cantú-Paz, E.: Personalized click prediction in sponsored search. In: ACM (2010)
Edizel, B., Mantrach, A., Bai, X.: Deep character-level click-through rate prediction for sponsored search. In: Stat.ml (2017)
Yadav, J., Sharma, M.: A review of K means algorithm. Int. J. Eng. Trends Technol. 4(7), 2972–2976 (2013)
Yanyun, C., Jianlin, Q., Xiang, G., Jianping, C., Dan, J., Li, C.: Advances in research of fuzzy c means algorithm. In: International Conference on Network Computing and Information Security (2011)
Bhatia, M.P.S., Kumar, A.: Information retrieval and machine learning: supporting technologies for web mining research and practice. Webology 5(2), 5 (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
Kumar, A., Nayyar, A., Upasani, S., Arora, A. (2020). Empirical Study of Soft Clustering Technique for Determining Click Through Rate in Online Advertising. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-32-9949-8_1
Download citation
DOI: https://doi.org/10.1007/978-981-32-9949-8_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9948-1
Online ISBN: 978-981-32-9949-8
eBook Packages: EngineeringEngineering (R0)