Volume 6 (2), December 2023, Pages 199-202

Suleyman Suleymanzade

Institute of Information Technology, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.


This research uses advanced regression techniques to develop a robust predictive model for Click-Through Rates (CTR) in online advertising. The study leverages a diverse dataset encompassing various advertising campaigns and user interactions to uncover patterns and relationships influencing click-through behavior. The goal is to provide advertisers with a tool for accurate CTR prediction, enabling them to optimize campaigns and allocate resources effectively.


Data Splitting, XGBoost, CTR-related, CTR Prediction.






Agarwal, D., Chen, B. C., & Elango, P. (2009, April). Spatio-temporal models for estimating click-through rate. In Proceedings of the 18th International Conference on World Wide Web (pp. 21-30).

Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree-boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).

Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: an interdisciplinary review. Journal of big data, 7(1), 1-45.

Kamal, M., & Bablu, T. A. (2022). Machine Learning Models for Predicting Click-through Rates on social media: Factors and Performance Analysis. International Journal of Applied Machine Learning and Computational Intelligence, 12(4), 1-14.

Kapoor, S., & Perrone, V. (2021). A Simple and Fast Baseline for Tuning Large XGBoost Models. arXiv preprint arXiv:2111.06924.

Richardson, M., Dominowska, E., & Ragno, R. (2007, May). Predicting clicks: estimating the click-through rate for new ads, in Proceedings of the 16th international conference2 on World Wide Web (pp. 521-530).

Saura, J. R. (2021). Using data sciences in digital marketing: Framework, methods, and performance metrics. Journal of Innovation & Knowledge, 6(2), 92-102.

Yang, Y., & Zhai, P. (2022). Click-through rate prediction in online advertising: A literature review. Information Processing & Management, 59(2), 102853.

Zhou, G. et al. (2019, July). Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 5941-5948).