Saltar al contenido
República del Perú · Observatorio de la producción científica universitaria
OApol.org Observatorio de Inteligencia Científica Universitaria del Perú
OApol.org
Inicio Universidades Publicaciones Investigadores
Repositorios
Tesis y trabajos académicosRevistas
Indicadores
Ranking trimestralRanking por carreraObjetivos de Desarrollo SostenibleReporte OCDERed neuronal 3D
Acerca
MetodologíaPrivacidad

Exploring the Influence of Machine Learning in e-Commerce: A Systematic and Bibliometric Review

Gamboa-Cruzado J.; Mosqueira-Cerda T.; Camones A.T.; Mendoza R.Q.; Navarro Raymundo A.F.; García J.J.; López-Ramírez B.C. · Computacion y Sistemas (2025) · DOI: 10.13053/cys-29-2-5749

Resumen

The use of Machine Learning (ML) in e-commerce has revolutionized key processes such as service personalization, dynamic pricing optimization, and sales forecasting, generating a direct impact on both operational efficiency and user experience quality. The objective of this paper is to rigorously identify, analyze, and synthesize the findings of the most relevant studies related to the application of Machine Learning and its impact within the field of e-Commerce. A systematic review was conducted on 66 papers extracted from recognized academic databases—Springer, Scopus, IEEE Xplore, Web of Science, and EBSCOhost—covering the period from 2018 to 2024. The methodology adopted was based on Kitchenham’s (2009) guidelines, with detailed documentation of search equations, exclusion criteria, and quality assessment parameters to ensure the consistency, transparency, and reliability of the results obtained. The thematic analysis revealed that the categories "Intelligent Detection" and "Advanced Machine Learning" are particularly prominent in the scientific literature. Furthermore, it was observed that papers published in higher-quartile journals tend to offer conclusions with a greater degree of objectivity and methodological rigor. It is recommended to promote interdisciplinary studies that leverage the high frequency of co-authorship identified, thereby fostering stronger scientific collaboration networks. Likewise, the homogeneity observed in paper titles reveals consolidated thematic lines, opening opportunities to explore innovative approaches in the field of e-commerce and machine learning.

Instituciones: Universidad Nacional Mayor de San Marcos; Universidad Nacional de Trujillo; Universidad Nacional Tecnológica de Lima Sur; Universidad Señor de Sipán