Abstract
Web advertising campaigns have the particularity that allow to measure the performance of campaigns based on different metrics, among which are the cost per thousand impressions (CPM-Cost Per mille), cost per click (CPC) and the click-to-print ratio (CTR-Click Through Ratio). For this reason, each ad has a specific objective based on these indicators which aim to distribute the purchase of advertising space on the Internet in the best possible way in order to have a better return on investment based on these metrics. The costs incurred in the development of its services is significant and the objectives of the campaigns are not always achieved because it assumes the variability of Internet user behavior. This project consists of proposing a regression model based on the historical data of the companies providing the programmatic purchasing service, in order to optimize negotiations on performance metrics in advertising campaigns with advertisers.
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References
Aladag, C., Hocaoglu, G.: A tabu search algorithm to solve a course timetabling problem. Hacettepe J. Math. Stat. 53–64 (2007)
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program (report 826) (1989)
Frausto-Solís, J., Alonso-Pecina, F., Mora-Vargas, J.: An efficient simulated annealing algorithm for feasible solutions of course timetabling. pp. 675–685. Springer (2008)
Joudaki, M., Imani, M., Mazhari, N.: Using Improved Memetic Algorithm and Local Search to Solve University Course Timetabling Problem (UCTTP). Islamic Azad University, Doroud, Iran (2010)
Coopers, P.W.H., IAB internet advertising revenue report. URL: http://www.iab.net/insights_research/industry_data_and_landscape/adrevenuereport (2014)
Tuzhilin, A.: The Lane’s Gifts v. Google Report. Official Google blog: Findings on invalid clicks. pp. 1–47 (2006)
Ponce, H., Ponce, P., Molina, A.: Artificial Organic Networks: Artificial Intelligence Based on Carbon Networks. Studies in Computational Intelligence, vol. 521. Springer (2014)
Ponce, H., Ponce, P., Molina, A.: A new training algorithm for artificial hydrocarbon networks using an energy model of covalent bonds. 7th IFAC Conf. Manuf. Model. Manag. Control. 7(1), 602–608 (2013)
Moe, W.W.: Targeting display advertising. Advanced database marketing: Innovative methodologies & applications for managing customer relationships. Londres: Gower Publishing (2013)
Stone-Gross, B., Stevens, R., Zarras, A., Kemmerer, R., Kruegel, C., Vigna, G.: Understanding fraudulent activities in online ad exchanges. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 279–294. ACM (2011)
McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J. Kubica, J.: Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1222–1230. ACM (2013)
Ponce, H., Ponce, P.: Artificial organic networks. In: IEEE Conference on Electronics, Robotics, and Automotive Mechanics CERMA, pp. 29–34. (2011)
Kuhn, M.: Building predictive models in R using the caret package. J. Stat. Softw. 28(5), 1–26 (2008)
Granitto, P.M., Furlanello, C., Biasioli, F., Gasperi, F.: Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometr. Intell. Lab. Syst. 83(2), 83–90 (2006)
Kuhn, W., Wing, J., Weston, S., Williams, A., Keefer, C., et al.: Caret: Classification and Regression Training. R package, vol. 515. (2012)
Miller, B., Pearce, P., Grier, C., Kreibich, C., Paxson, V.: What’s clicking what? Techniques and innovations of today’s clickbots. In: Detection of Intrusions and Malware, and Vulnerability Assessment, pp. 164–183. Springer (2011)
Kamatkar, S. J., Tayade, A., Viloria, A., Hernández-Chacín, A.: Application of classification technique of data mining for employee management system. In International Conference on Data Mining and Big Data, pp. 434–444. Springer, Cham (2018, June)
Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., Cali, E.G.: Database performance tuning and query optimization. In: International Conference on Data Mining and Big Data, pp. 3–11. Springer, Cham (2018, June)
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Silva, J. et al. (2020). CTR Prediction for Optimizing the Negotiation of Internet Advertising Campaigns. In: Rocha, Á., Paredes-Calderón, M., Guarda, T. (eds) Developments and Advances in Defense and Security. MICRADS 2020. Smart Innovation, Systems and Technologies, vol 181. Springer, Singapore. https://doi.org/10.1007/978-981-15-4875-8_13
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DOI: https://doi.org/10.1007/978-981-15-4875-8_13
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