A Recommender System for Digital Newspaper Readers Based on Random Forest
In this research, the potential of machine learning methods based on decision trees (DT) and Random Forest (RF) models developed in the context of classifying readers of a digital newspaper. For this purpose, the number of visits of users to each section of the newspaper in a 3-month interval has be...
- Autores:
-
Delahoz-Dominguez, Enrique
Zuluaga Ortiz, Rohemi Alfredo
Mendoza-Mendoza, Adel
Escorcia, Jey
Moreira-Villegas, Francisco
Oliveros-Eusse, Pedro
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12106
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12106
- Palabra clave:
- Customer Churn;
Sales;
Customer Relationship Management
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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A Recommender System for Digital Newspaper Readers Based on Random Forest |
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A Recommender System for Digital Newspaper Readers Based on Random Forest |
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A Recommender System for Digital Newspaper Readers Based on Random Forest Customer Churn; Sales; Customer Relationship Management LEMB |
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A Recommender System for Digital Newspaper Readers Based on Random Forest |
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A Recommender System for Digital Newspaper Readers Based on Random Forest |
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A Recommender System for Digital Newspaper Readers Based on Random Forest |
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A Recommender System for Digital Newspaper Readers Based on Random Forest |
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A Recommender System for Digital Newspaper Readers Based on Random Forest |
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Delahoz-Dominguez, Enrique Zuluaga Ortiz, Rohemi Alfredo Mendoza-Mendoza, Adel Escorcia, Jey Moreira-Villegas, Francisco Oliveros-Eusse, Pedro |
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Delahoz-Dominguez, Enrique Zuluaga Ortiz, Rohemi Alfredo Mendoza-Mendoza, Adel Escorcia, Jey Moreira-Villegas, Francisco Oliveros-Eusse, Pedro |
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Customer Churn; Sales; Customer Relationship Management |
| topic |
Customer Churn; Sales; Customer Relationship Management LEMB |
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LEMB |
| description |
In this research, the potential of machine learning methods based on decision trees (DT) and Random Forest (RF) models developed in the context of classifying readers of a digital newspaper. For this purpose, the number of visits of users to each section of the newspaper in a 3-month interval has been taken into account. The models of DT and RF developed in this paper classify the profiles of readers who access the journal with an accuracy of 98.07% and AUC value of 99.27%, thus demonstrating that it serves as a valid tool for making strategic and operational decisions when creating, manage and present content in the user – website interaction. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
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2022 |
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2022 |
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2023-07-14T13:53:26Z |
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2023-07-14T13:53:26Z |
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2023 |
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Delahoz-Dominguez, E., Zuluaga-Ortiz, R., Mendoza-Mendoza, A., Escorcia, J., Moreira-Villegas, F., & Oliveros-Eusse, P. (2022). A recommender system for digital newspaper readers based on random forest. En Computer Information Systems and Industrial Management (pp. 191–201). Springer International Publishing. |
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https://hdl.handle.net/20.500.12585/12106 |
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DOI 10.1007/978-3-031-10539-5_14 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Delahoz-Dominguez, E., Zuluaga-Ortiz, R., Mendoza-Mendoza, A., Escorcia, J., Moreira-Villegas, F., & Oliveros-Eusse, P. (2022). A recommender system for digital newspaper readers based on random forest. En Computer Information Systems and Industrial Management (pp. 191–201). Springer International Publishing. DOI 10.1007/978-3-031-10539-5_14 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12106 |
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eng |
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eng |
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10 páginas |
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Computer Information Systems and Industrial Management (pp. 191–201). Springer International Publishing. |
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Delahoz-Dominguez, Enrique1845b064-8f16-48b4-8210-4a40985833e2Zuluaga Ortiz, Rohemi Alfredo182b4086-4094-4784-9843-4be10643fb58Mendoza-Mendoza, Adeld6f952c6-8a50-46a1-a2e9-6920562be222Escorcia, Jey2dcb7aab-5c50-4d03-911c-1c90124828d7Moreira-Villegas, Francisco137ce9a7-036f-4dee-86af-3d12c327d1caOliveros-Eusse, Pedrofde1ee07-4f59-4233-993c-cc497ec530f42023-07-14T13:53:26Z2023-07-14T13:53:26Z20222023Delahoz-Dominguez, E., Zuluaga-Ortiz, R., Mendoza-Mendoza, A., Escorcia, J., Moreira-Villegas, F., & Oliveros-Eusse, P. (2022). A recommender system for digital newspaper readers based on random forest. En Computer Information Systems and Industrial Management (pp. 191–201). Springer International Publishing.https://hdl.handle.net/20.500.12585/12106DOI 10.1007/978-3-031-10539-5_14Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIn this research, the potential of machine learning methods based on decision trees (DT) and Random Forest (RF) models developed in the context of classifying readers of a digital newspaper. For this purpose, the number of visits of users to each section of the newspaper in a 3-month interval has been taken into account. The models of DT and RF developed in this paper classify the profiles of readers who access the journal with an accuracy of 98.07% and AUC value of 99.27%, thus demonstrating that it serves as a valid tool for making strategic and operational decisions when creating, manage and present content in the user – website interaction. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.10 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Computer Information Systems and Industrial Management (pp. 191–201). Springer International Publishing.A Recommender System for Digital Newspaper Readers Based on Random Forestinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Customer Churn;Sales;Customer Relationship ManagementLEMBCartagena de IndiasEl Naqa, I., Murphy, M.J. What is machine learning? (2015) Machine Learning in Radiation Oncology, pp. 3-11. Cited 319 times. El Naqa, I., Li, R., Murphy, M.J. (eds.), Springer, Cham https://doi.org/10.1007/978-3-319-18305-3_1Hoz, E.D.L., Zuluaga, R., Mendoza, A. Assessing and classification of academic efficiency in engineering teaching programs (2021) Journal on Efficiency and Responsibility in Education and Science, 14 (1), pp. 41-52. Cited 13 times. https://www.eriesjournal.com/index.php/eries/article/view/375 doi: 10.7160/ERIESJ.2021.140104Escorcia Guzman, J.H., Zuluaga-Ortiz, R.A., Barrios-Miranda, D.A., Delahoz-Dominguez, E.J. Information and Communication Technologies (ICT) in the processes of distribution and use of knowledge in Higher Education Institutions (HEIs) (2021) Procedia Computer Science, 198, pp. 644-649. Cited 6 times. http://www.sciencedirect.com/science/journal/18770509 doi: 10.1016/j.procs.2021.12.300Suthaharan, S. Big data classification: Problems and challenges in network intrusion prediction with machine learning (2014) Performance Evaluation Review, 41 (4), pp. 70-73. Cited 273 times. http://portal.acm.org/browse_dl.cfm?linked=1&part=newsletter&idx=J618&coll=portal&dl=ACM&CFID=57809500&CFTOKEN=27978298 doi: 10.1145/2627534.2627557Nayak, A., Dutta, K. Impacts of machine learning and artificial intelligence on mankind (2018) Proceedings of 2017 International Conference on Intelligent Computing and Control, I2C2 2017, 2018-January, pp. 1-3. Cited 27 times. ISBN: 978-153860374-1 doi: 10.1109/I2C2.2017.8321908Obermeyer, Z., Emanuel, E.J. Predicting the future-big data, machine learning, and clinical medicine (2016) New England Journal of Medicine, 375 (13), pp. 1216-1219. Cited 1519 times. http://www.nejm.org/doi/pdf/10.1056/NEJMp1606181 doi: 10.1056/NEJMp1606181Yu, Q., Miche, Y., Séverin, E., Lendasse, A. Bankruptcy prediction using Extreme Learning Machine and financial expertise (2014) Neurocomputing, 128, pp. 296-302. Cited 100 times. doi: 10.1016/j.neucom.2013.01.063Mahdavinejad, M.S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., Sheth, A.P. Machine learning for internet of things data analysis: a survey (2018) Digital Communications and Networks, 4 (3), pp. 161-175. Cited 602 times. https://www.journals.elsevier.com/digital-communications-and-networks doi: 10.1016/j.dcan.2017.10.002De La Hoz, E.J., De La Hoz, E.J., Fontalvo, T.J. Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments (2019) Informacion Tecnologica, 30 (1), pp. 247-254. Cited 23 times. https://scielo.conicyt.cl/pdf/infotec/v30n1/0718-0764-infotec-30-01-247.pdf doi: 10.4067/S0718-07642019000100247Delahoz-Dominguez, E.J., Fontalvo, T., Zuluaga, R. Evaluation of academic productivity of citizen competencies in the teaching of engineering by using the Malmquist index (Open Access) (2020) Formacion Universitaria, 13 (5), pp. 27-34. Cited 5 times. http://www.scielo.cl/scielo.php?script=sci_serial&pid=0718-5006&lng=en&nrm=iso doi: 10.4067/S0718-50062020000500027Delahoz-Dominguez, E., Zuluaga, R., Fontalvo-Herrera, T. Dataset of academic performance evolution for engineering students (Open Access) (2020) Data in Brief, 30, art. no. 105537. Cited 17 times. https://www.journals.elsevier.com/data-in-brief doi: 10.1016/j.dib.2020.105537Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I. Machine learning applications in cancer prognosis and prediction (2015) Computational and Structural Biotechnology Journal, 13, pp. 8-17. Cited 1652 times. www.csbj.org doi: 10.1016/j.csbj.2014.11.005Erevelles, S., Fukawa, N., Swayne, L. Big Data consumer analytics and the transformation of marketing (Open Access) (2016) Journal of Business Research, 69 (2), pp. 897-904. Cited 717 times. http://www.elsevier.com/locate/jbusres doi: 10.1016/j.jbusres.2015.07.001Stalidis, G., Karapistolis, D., Vafeiadis, A. Marketing decision support using artificial intelligence and knowledge modeling: Application to tourist destination management (2015) Procedia Soc. Behav. Sci., 175, pp. 106-113. 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