CTR prediction for optimizing the negotiation of internet advertising campaigns
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 a...
- Autores:
-
Silva, Jesús
Vargas, Jesús
Rizzo-Vergara, Dawin
Araya Ugarte, Guillermo Agustín
Rosado, César Enrique
Pineda, Omar
Quintero, Benjamín
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7739
- Acceso en línea:
- https://hdl.handle.net/11323/7739
https://doi.org/10.1007/978-981-15-4875-8_13
https://repositorio.cuc.edu.co/
- Palabra clave:
- Smart cities
Wireless sensor networks
Internet of things
Wireless nodes
Communication architecture
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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CTR prediction for optimizing the negotiation of internet advertising campaigns |
| title |
CTR prediction for optimizing the negotiation of internet advertising campaigns |
| spellingShingle |
CTR prediction for optimizing the negotiation of internet advertising campaigns Smart cities Wireless sensor networks Internet of things Wireless nodes Communication architecture |
| title_short |
CTR prediction for optimizing the negotiation of internet advertising campaigns |
| title_full |
CTR prediction for optimizing the negotiation of internet advertising campaigns |
| title_fullStr |
CTR prediction for optimizing the negotiation of internet advertising campaigns |
| title_full_unstemmed |
CTR prediction for optimizing the negotiation of internet advertising campaigns |
| title_sort |
CTR prediction for optimizing the negotiation of internet advertising campaigns |
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Silva, Jesús Vargas, Jesús Rizzo-Vergara, Dawin Araya Ugarte, Guillermo Agustín Rosado, César Enrique Pineda, Omar Quintero, Benjamín |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Vargas, Jesús Rizzo-Vergara, Dawin Araya Ugarte, Guillermo Agustín Rosado, César Enrique Pineda, Omar Quintero, Benjamín |
| dc.subject.spa.fl_str_mv |
Smart cities Wireless sensor networks Internet of things Wireless nodes Communication architecture |
| topic |
Smart cities Wireless sensor networks Internet of things Wireless nodes Communication architecture |
| description |
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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2020 |
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2020 |
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2021-01-21T13:38:32Z |
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2021-01-21T13:38:32Z |
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eng |
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1. Aladag, C., Hocaoglu, G.: A tabu search algorithm to solve a course timetabling problem. Hacettepe J. Math. Stat. 53–64 (2007) 2. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program (report 826) (1989) 3. 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) 4. 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) 5. Coopers, P.W.H., IAB internet advertising revenue report. URL: http://www.iab.net/insights_research/industry_data_and_landscape/adrevenuereport (2014) 6. Tuzhilin, A.: The Lane’s Gifts v. Google Report. Official Google blog: Findings on invalid clicks. pp. 1–47 (2006) 7. Ponce, H., Ponce, P., Molina, A.: Artificial Organic Networks: Artificial Intelligence Based on Carbon Networks. Studies in Computational Intelligence, vol. 521. Springer (2014) 8. 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) 9. Moe, W.W.: Targeting display advertising. Advanced database marketing: Innovative methodologies & applications for managing customer relationships. Londres: Gower Publishing (2013) 10. 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) 11. 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) 12. Ponce, H., Ponce, P.: Artificial organic networks. In: IEEE Conference on Electronics, Robotics, and Automotive Mechanics CERMA, pp. 29–34. (2011) 13. Kuhn, M.: Building predictive models in R using the caret package. J. Stat. Softw. 28(5), 1–26 (2008) 14. 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) 15. Kuhn, W., Wing, J., Weston, S., Williams, A., Keefer, C., et al.: Caret: Classification and Regression Training. R package, vol. 515. (2012) 16. 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) 17. 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) 18. 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, JesúsVargas, JesúsRizzo-Vergara, DawinAraya Ugarte, Guillermo AgustínRosado, César EnriquePineda, OmarQuintero, Benjamín2021-01-21T13:38:32Z2021-01-21T13:38:32Z2020https://hdl.handle.net/11323/7739https://doi.org/10.1007/978-981-15-4875-8_13Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Silva, JesúsVargas, JesúsRizzo-Vergara, Dawin-will be generated-orcid-0000-0001-8778-5039-600Araya Ugarte, Guillermo Agustín-will be generated-orcid-0000-0001-9068-8053-600Rosado, César Enrique-will be generated-orcid-0000-0002-9064-7451-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Quintero, Benjamínapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Smart Innovation, Systems and Technologieshttps://link.springer.com/chapter/10.1007/978-981-15-4875-8_13Smart citiesWireless sensor networksInternet of thingsWireless nodesCommunication architectureCTR prediction for optimizing the negotiation of internet advertising campaignsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Aladag, C., Hocaoglu, G.: A tabu search algorithm to solve a course timetabling problem. Hacettepe J. Math. Stat. 53–64 (2007)2. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program (report 826) (1989)3. 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)4. 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)5. Coopers, P.W.H., IAB internet advertising revenue report. URL: http://www.iab.net/insights_research/industry_data_and_landscape/adrevenuereport (2014)6. Tuzhilin, A.: The Lane’s Gifts v. Google Report. Official Google blog: Findings on invalid clicks. pp. 1–47 (2006)7. Ponce, H., Ponce, P., Molina, A.: Artificial Organic Networks: Artificial Intelligence Based on Carbon Networks. Studies in Computational Intelligence, vol. 521. Springer (2014)8. 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)9. Moe, W.W.: Targeting display advertising. Advanced database marketing: Innovative methodologies & applications for managing customer relationships. Londres: Gower Publishing (2013)10. 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)11. 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)12. Ponce, H., Ponce, P.: Artificial organic networks. In: IEEE Conference on Electronics, Robotics, and Automotive Mechanics CERMA, pp. 29–34. (2011)13. Kuhn, M.: Building predictive models in R using the caret package. J. Stat. Softw. 28(5), 1–26 (2008)14. 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)15. Kuhn, W., Wing, J., Weston, S., Williams, A., Keefer, C., et al.: Caret: Classification and Regression Training. R package, vol. 515. (2012)16. 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)17. 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)18. 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. 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