Click Through Rate Prediction Employing Wavelet Tree and Regression Learning

Meghna Chandel¹ , Sanjay Silakari² , Rajeev Pandey³ , Smita Sharma⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.5 , pp.45-51, Oct-2022

Online published on Oct 31, 2022

Copyright © Meghna Chandel, Sanjay Silakari, Rajeev Pandey, Smita Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Click through rates have proven to be a critical factor in deciding the effectiveness of online advertising models. Sponsored search advertising, contextual advertising, display advertising, and real-time bidding auctions have all relied heavily on the ability of learned models to predict ad click–through rates accurately, quickly, and reliably. Forecasting ad click–through rates (CTR) is a massive-scale learning problem that is central to the multi -billion dollar online advertising industry. Search engine advertising has become a significant element of the web browsing experience. Choosing the right ads for a query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. Accurately estimating the click-through rate (CTR) of ads has a vital impact on the revenue of search businesses; even a 0.1% accuracy improvement in production would yield hundreds of millions of dollars in additional earnings. An ad’s CTR is usually modelled as a forecasting problem, and thus can be estimated by machine learning models. The training data is collected from historical ads impressions and the corresponding clicks. An estimate of click through prior to fetching an add for a query is important for the accurate decision in the context. In this work a recursive binary partitioning algorithm is used along with support vector regression to estimate the bipolar nature of add clicks. A comparative analysis has also been made with exiting baseline techniques and it has been found that the proposed approach attains better performance metrics compared to baseline techniques.

Key-Words / Index Term :
Online Advertising, Data Mining, Click Through Rates (CTR), Wavelet Tree, Regression Learning, Support Vector Regression, Prediction Accuracy

References :
[1] JA Choi, K Lim, “Identifying machine learning techniques for classification of target advertising”, ICT Express, Elsevier, Vol. 6, Issue.3, pp.175-180, 2020.
[2] M. Gan and K. Xiao, "R-RNN: Extracting User Recent Behavior Sequence for Click-Through Rate Prediction," in IEEE Access, Vol. 7, pp.111767-111777, 2019.
[3] Q. Wang, F. Liu, P. Huang, S. Xing and X. Zhao, "A Hierarchical Attention Model for CTR Prediction Based on User Interest," in IEEE Systems Journal, Vol.14, Issue.3, pp.4015-4024, 2019.
[4] L. Y. Akella, "Ad-Blockers — Rising threat to digital content: Business analytics study," 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), pp.324-332, 2017.
[5] G. Chauhan and D. V. Mishra, "Evaluating deep learning based models for predicting click through rate," 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), pp.1-5, 2019.
[6] X Wang, G Hu, H Lin, J Sun, “A novel ensemble approach for click-through rate prediction based on factorization machines and gradient boosting decision trees”, APWeb-WAIM 2019: Web and Big Data, Springer, pp.152–162, 2019.
[7] Z Xiao, L Yang, W Jiang, Y Wei, Y Hu, “Deep multi-interest network for click-through rate prediction”, CIKM `20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, ACM, pp.2265-2268, 2020.
[8] J Gligorijevic, J .Gligorijevic., D. Stojkovic, “Deeply supervised model for click-through rate prediction in sponsored search” Data Min Knowledge Discovery, Springer, Vol.33, pp:1446–1467, 2019.
[9] L. Zhang, W. Shen, J. Huang, S. Li and G. Pan, "Field-Aware Neural Factorization Machine for Click-Through Rate Prediction," in IEEE Access, Vol.7, pp.75032-75040, 2019.
[10] X. Qu, L. Li, X. Liu, R. Chen, Y. Ge and S. -H. Choi, "A Dynamic Neural Network Model for Click-Through Rate Prediction in Real-Time Bidding," 2019 IEEE International Conference on Big Data (Big Data), pp.1887-1896, 2019.
[11] S. Zhang, Z. Liu and W. Xiao, "A Hierarchical Extreme Learning Machine Algorithm for Advertisement Click-Through Rate Prediction," in IEEE Access, Vol.6, pp.50641-50647, 2018.
[12] J Dhanani, K Rana, “Logistic Regression with Stochastic Gradient Ascent to Estimate Click Through Rate”, Information and Communication Technology for Sustainable Development, Springer, pp.319-326, 2018.