CTR prediction of internet ads using artificial organic networks
For advertising networks to increase their revenues, priority must be given to the most profitable ads. The most important factor in the profitability of an ad is the click-through-rate (CTR) which is the probability that a user will click on the ad on a Web page. To predict the CTR, a number of sup...
- Autores:
-
Silva, Jesús
Varela Izquierdo, Noel
Cabrera, Danelys
Pineda, Omar
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7280
- Acceso en línea:
- https://hdl.handle.net/11323/7280
https://repositorio.cuc.edu.co/
- Palabra clave:
- Artificial organic networks in advertising
CPC advertising networks
CTR prediction
Supervised rating models
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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CTR prediction of internet ads using artificial organic networks |
| title |
CTR prediction of internet ads using artificial organic networks |
| spellingShingle |
CTR prediction of internet ads using artificial organic networks Artificial organic networks in advertising CPC advertising networks CTR prediction Supervised rating models |
| title_short |
CTR prediction of internet ads using artificial organic networks |
| title_full |
CTR prediction of internet ads using artificial organic networks |
| title_fullStr |
CTR prediction of internet ads using artificial organic networks |
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CTR prediction of internet ads using artificial organic networks |
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CTR prediction of internet ads using artificial organic networks |
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Silva, Jesús Varela Izquierdo, Noel Cabrera, Danelys Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Varela Izquierdo, Noel Cabrera, Danelys Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Artificial organic networks in advertising CPC advertising networks CTR prediction Supervised rating models |
| topic |
Artificial organic networks in advertising CPC advertising networks CTR prediction Supervised rating models |
| description |
For advertising networks to increase their revenues, priority must be given to the most profitable ads. The most important factor in the profitability of an ad is the click-through-rate (CTR) which is the probability that a user will click on the ad on a Web page. To predict the CTR, a number of supervised rating models have been trained and their performance is compared to artificial organic networks (AON). The conclusion is that these networks are a good solution to predict the CTR of an ad. |
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2020 |
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2020-11-12T17:37:13Z |
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2020-11-12T17:37:13Z |
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2020 |
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2021-01-31 |
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eng |
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Aladag C, Hocaoglu G (2007) A tabu search algorithm to solve a course timetabling problem. Hacettepe J Math Stat pp 53–64 Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. caltech concurrent computation program (report 826) Frausto-Solís J, Alonso-Pecina F, Mora-Vargas J (2008) An efficient simulated annealing algorithm for feasible solutions of course timetabling. Springer, pp 675–685 Joudaki M, Imani M, Mazhari N (2010) Using improved Memetic algorithm and local search to solve University Course Timetabling Problem (UCTTP). Islamic Azad University, Doroud, Iran Coopers PWH (2014) IAB internet advertising revenue report. http://www.iab.net/insights_research/industry_data_and_landscape/adrevenuereport Tuzhilin A (2006) The lane’s gifts v. google report. Official Google Blog: Findings on invalid clicks, pp 1–47 Ponce H, Ponce P, Molina A (2014) Artificial organic networks: artificial intelligence based on carbon networks. Studies in computational intelligence, vol. 521, Springer Ponce H, Ponce P, Molina A (2013) A new training algorithm for artificial hydrocarbon networks using an energy model of covalent bonds. In: 7th IFAC conference on manufacturing modelling, management, and control, vol 7(1), pp 602–608 Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham Moe WW (2013) Targeting display advertising. Advanced database marketing: Innovative methodologies and applications for managing customer relationships. Gower Publishing, Londres Stone-Gross B, Stevens R, Zarras A, Kemmerer R, Kruegel C, Vigna G (2011) Understanding fraudulent activities in online ad exchanges. In: Proceedings of the 2011 ACM SIGCOMM conference on internet measurement conference. ACM, pp 279–294 McMahan HB, Holt G, Sculley D, Young M, Ebner D, Grady J, Kubica J (2013) Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1222–1230 Ponce H, Ponce P (2011) Artificial organic networks. In: IEEE conference on electronics, robotics, and automotive mechanics CERMA, pp 29–34 Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28(5):1–26 Granitto PM, Furlanello C, Biasioli F, Gasperi F (2006) Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometr Intell Lab Syst 83(2):83–90 Kuhn W, Wing J, Weston S, Williams A, Keefer C et al (2012) Caret: classification and regression training. R package 515 Miller B, Pearce P, Grier C, Kreibich C, Paxson V (2011) What’s clicking what? Techniques and innovations of today’s clickbots. In: Detection of intrusions and malware, and vulnerability assessment. Springer, pp. 164–183 Kamatkar SJ, Tayade A, Viloria A, Hernández-Chacín A (2018) Application of classification technique of data mining for employee management system. In: International conference on data mining and big data. Springer, Cham, pp 434–444 Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data. Springer, Cham, pp 3–11 |
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Silva, JesúsVarela Izquierdo, NoelCabrera, DanelysPineda, Omar2020-11-12T17:37:13Z2020-11-12T17:37:13Z20202021-01-312194-5357https://hdl.handle.net/11323/7280Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/For advertising networks to increase their revenues, priority must be given to the most profitable ads. The most important factor in the profitability of an ad is the click-through-rate (CTR) which is the probability that a user will click on the ad on a Web page. To predict the CTR, a number of supervised rating models have been trained and their performance is compared to artificial organic networks (AON). The conclusion is that these networks are a good solution to predict the CTR of an ad.Silva, JesúsVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Cabrera, Danelys-will be generated-orcid-0000-0002-9486-9764-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090094053&doi=10.1007%2f978-981-15-6876-3_38&partnerID=40&md5=7083b813245ab891a09c08f2e320d3f6Artificial organic networks in advertisingCPC advertising networksCTR predictionSupervised rating modelsCTR prediction of internet ads using artificial organic networksPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionAladag C, Hocaoglu G (2007) A tabu search algorithm to solve a course timetabling problem. Hacettepe J Math Stat pp 53–64Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. caltech concurrent computation program (report 826)Frausto-Solís J, Alonso-Pecina F, Mora-Vargas J (2008) An efficient simulated annealing algorithm for feasible solutions of course timetabling. Springer, pp 675–685Joudaki M, Imani M, Mazhari N (2010) Using improved Memetic algorithm and local search to solve University Course Timetabling Problem (UCTTP). Islamic Azad University, Doroud, IranCoopers PWH (2014) IAB internet advertising revenue report. http://www.iab.net/insights_research/industry_data_and_landscape/adrevenuereportTuzhilin A (2006) The lane’s gifts v. google report. Official Google Blog: Findings on invalid clicks, pp 1–47Ponce H, Ponce P, Molina A (2014) Artificial organic networks: artificial intelligence based on carbon networks. Studies in computational intelligence, vol. 521, SpringerPonce H, Ponce P, Molina A (2013) A new training algorithm for artificial hydrocarbon networks using an energy model of covalent bonds. In: 7th IFAC conference on manufacturing modelling, management, and control, vol 7(1), pp 602–608Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, ChamMoe WW (2013) Targeting display advertising. Advanced database marketing: Innovative methodologies and applications for managing customer relationships. Gower Publishing, LondresStone-Gross B, Stevens R, Zarras A, Kemmerer R, Kruegel C, Vigna G (2011) Understanding fraudulent activities in online ad exchanges. In: Proceedings of the 2011 ACM SIGCOMM conference on internet measurement conference. ACM, pp 279–294McMahan HB, Holt G, Sculley D, Young M, Ebner D, Grady J, Kubica J (2013) Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1222–1230Ponce H, Ponce P (2011) Artificial organic networks. In: IEEE conference on electronics, robotics, and automotive mechanics CERMA, pp 29–34Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28(5):1–26Granitto PM, Furlanello C, Biasioli F, Gasperi F (2006) Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometr Intell Lab Syst 83(2):83–90Kuhn W, Wing J, Weston S, Williams A, Keefer C et al (2012) Caret: classification and regression training. R package 515Miller B, Pearce P, Grier C, Kreibich C, Paxson V (2011) What’s clicking what? Techniques and innovations of today’s clickbots. In: Detection of intrusions and malware, and vulnerability assessment. Springer, pp. 164–183Kamatkar SJ, Tayade A, Viloria A, Hernández-Chacín A (2018) Application of classification technique of data mining for employee management system. In: International conference on data mining and big data. Springer, Cham, pp 434–444Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data. 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