A Recommender System for Digital Newspaper Readers Based on Random Forest
Fecha
2022Registro en:
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
Autor
Delahoz-Dominguez, Enrique
Zuluaga-Ortiz, Rohemi
Mendoza-Mendoza, Adel
Escorcia, Jey
Moreira-Villegas, Francisco
Oliveros-Eusse, Pedro
Resumen
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.