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A Recommender System for Digital Newspaper Readers Based on Random Forest

dc.contributor.authorDelahoz-Dominguez, Enrique
dc.contributor.authorZuluaga Ortiz, Rohemi Alfredo
dc.contributor.authorMendoza-Mendoza, Adel
dc.contributor.authorEscorcia, Jey
dc.contributor.authorMoreira-Villegas, Francisco
dc.contributor.authorOliveros-Eusse, Pedro
dc.date.accessioned2023-07-14T13:53:26Z
dc.date.available2023-07-14T13:53:26Z
dc.date.issued2022
dc.date.submitted2023
dc.identifier.citationDelahoz-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.spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/12106
dc.description.abstractIn 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.spa
dc.format.extent10 páginas
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceComputer Information Systems and Industrial Management (pp. 191–201). Springer International Publishing.spa
dc.titleA Recommender System for Digital Newspaper Readers Based on Random Forestspa
dcterms.bibliographicCitationEl 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_1spa
dcterms.bibliographicCitationHoz, 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.2021.140104spa
dcterms.bibliographicCitationEscorcia 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.2021.12.300spa
dcterms.bibliographicCitationSuthaharan, 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: 10.1145/2627534.2627557spa
dcterms.bibliographicCitationNayak, 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/I2C2.2017.8321908spa
dcterms.bibliographicCitationObermeyer, 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/NEJMp1606181spa
dcterms.bibliographicCitationYu, 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.2013.01.063spa
dcterms.bibliographicCitationMahdavinejad, 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.2017.10.002spa
dcterms.bibliographicCitationDe 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/S0718-07642019000100247spa
dcterms.bibliographicCitationDelahoz-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/S0718-50062020000500027spa
dcterms.bibliographicCitationDelahoz-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.2020.105537spa
dcterms.bibliographicCitationKourou, 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.2014.11.005spa
dcterms.bibliographicCitationErevelles, 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.2015.07.001spa
dcterms.bibliographicCitationStalidis, 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. Cited 38 times.spa
dcterms.bibliographicCitationSundsøy, P., Bjelland, J., Iqbal, A.M., Pentland, A., De Montjoye, Y.-A. Big data-driven marketing: How machine learning outperforms marketers' gut-feeling (Open Access) (2014) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8393 LNCS, pp. 367-374. Cited 29 times. http://springerlink.com/content/0302-9743/copyright/2005/ ISBN: 978-331905578-7 doi: 10.1007/978-3-319-05579-4_45spa
dcterms.bibliographicCitationF.Y, O., J.E.T, A., O, A., J.O, H., O, O., J, A. Supervised machine learning algorithms: Classification and comparison (2017) Int. J. Comput. Trends Technol., 48 (3), pp. 128-138. Cited 316 times.spa
dcterms.bibliographicCitationAllcott, H., Gentzkow, M. Social media and fake news in the 2016 election (2017) Journal of Economic Perspectives, 31 (2), pp. 211-236. Cited 2715 times. http://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.31.2.211 doi: 10.1257/jep.31.2.211spa
dcterms.bibliographicCitationAdgate, B. Newspapers have Been Struggling and then Came the Pandemic. Cited 2 times. https://www.forbes.com/sites/bradadgate/2021/08/20/newspapers-have-been-struggling-and-then-camethe-pandemic/spa
dcterms.bibliographicCitationDigital Transformation through Data for News and Media Companies http://www2.deloitte.com/us/en/pages/consulting/articles/digital-transformation-through-data-fornews.htmlspa
dcterms.bibliographicCitationde La Hoz Domínguez, E., Mendoza Mendoza, A., Ojeda De La Hoz, H. Classification of readers profiles of a digital journal (2017) Rev. UDCA Actual. Amp Divulg. Científica., 20, pp. 469-478. Cited 4 times.spa
dcterms.bibliographicCitationAhn, J., Jung, Y. The common sense of dependence on smartphone: A comparison between digital natives and digital immigrants (2016) New Media and Society, 18 (7), pp. 1236-1256. Cited 64 times. doi: 10.1177/1461444814554902spa
dcterms.bibliographicCitationHernández, D.H., Ramírez-Martinell, A., Cassany, D. Categorizando a los usuarios de sistemas digitales (2014) Pixel-Bit Rev. Medios Educ.. Cited 13 times. https://doi.org/10.12795/pixelbit.2014.i44.08spa
dcterms.bibliographicCitationTherneau, T., Atkinson, B., Ripley, B. rpart: Recursive partitioning and regression trees (Version R package version 4.1-10). URL HttpsCRAN R-Proj (2015) Orgpackage Rpartspa
dcterms.bibliographicCitationEsposito, F., Malerba, D., Semeraro, G. A comparative analysis of methods for pruning decision trees (Open Access) (1997) IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (5), pp. 476-491. Cited 409 times. doi: 10.1109/34.589207spa
dcterms.bibliographicCitationIBM: Pruning Decision Tress https://prod.ibmdocs-production-dal-6099123ce774e592a519d7c33db8265e-0000.us-south.containers.appdomain.cloud/docs/en/db2/10.5?topic=view-pruning-decision-treesspa
dcterms.bibliographicCitationPetkovic, D., Altman, R., Wong, M., Vigil, A. Improving the explainability of Random Forest classifier – User centered approach (Open Access) (2018) Pacific Symposium on Biocomputing, 0, pp. 204-215. Cited 47 times. http://psb.stanford.edu/ doi: 10.1142/9789813235533_0019spa
dcterms.bibliographicCitationBreiman, L. Random forests (Open Access) (2001) Machine Learning, 45 (1), pp. 5-32. Cited 69880 times. doi: 10.1023/A:1010933404324spa
dcterms.bibliographicCitationBiau, G. Analysis of a random forests model (2012) Journal of Machine Learning Research, 13, pp. 1063-1095. Cited 869 times. http://jmlr.csail.mit.edu/papers/volume13/biau12a/biau12a.pdfspa
dcterms.bibliographicCitationAraújo, F.H.D., Santana, A.M., de A. Santos Neto, P. Using machine learning to support healthcare professionals in making preauthorisation decisions (Open Access) (2016) International Journal of Medical Informatics, 94, pp. 1-7. Cited 38 times. www.elsevier.com/inca/publications/store/5/0/6/0/4/0/ doi: 10.1016/j.ijmedinf.2016.06.007spa
dcterms.bibliographicCitationFaraggi, D., Reiser, B. Estimation of the area under the ROC curve (Open Access) (2002) Statistics in Medicine, 21 (20), pp. 3093-3106. Cited 311 times. doi: 10.1002/sim.1228spa
dcterms.bibliographicCitationAdeniyi, D.A., Wei, Z., Yongquan, Y. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method (Open Access) (2016) Applied Computing and Informatics, 12 (1), pp. 90-108. Cited 266 times. https://www.emeraldgrouppublishing.com/journal/aci doi: 10.1016/j.aci.2014.10.001spa
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dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasversioninfo:eu-repo/semantics/draftspa
dc.identifier.doiDOI 10.1007/978-3-031-10539-5_14
dc.subject.keywordsCustomer Churn;spa
dc.subject.keywordsSales;spa
dc.subject.keywordsCustomer Relationship Managementspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.subject.armarcLEMB
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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.