Machine learning applications in digital advertising performance optimization: A systematic literature review

Apaza-Caceres J.A.; Paja L.A.V.; Huachaca A.P.; Choque D.A.P. · International Journal of Data and Network Science (2026) · DOI: 10.5267/j.ijdns.2025.9.020

Resumen

The explosion of growth in digital advertising, reaching $798.7 billion by 2025 with 72% of companies adopting artificial intelligence, warrants a systematic understanding of how machine learning is transforming this sector. The objective was to systematically analyze the application of machine learning in web advertising campaigns through a comprehensive review of scientific literature (2010-2025). The PRISMA methodology was implemented with five-dimensional quality criteria (0-3 points), selecting 42 excellent articles (13-15 points). The results reveal a dominance of deep learning (66.6%), with Deep Neural Networks (35.7%) and attention models (19.0%) predominating; convergence toward CTR as a universal metric (95.2%); concentration in e-commerce (61.9%), led by Alibaba (14.3%); and data sparsity as a fundamental limitation (59.5%). Significant algorithmic consolidation is found, but critical gaps in fairness (0%), sustainability (0%), and robustness (0%). Implications include the need for methodological diversification, the development of equity-aware frameworks, and expansion into sectors regulated by privacy-preserving techniques.

Instituciones: Universidad Autónoma del Perú