A Multi-Dimensional Approach to Understanding the Effect of Page Content and Infrastructure on Page Load Time - Oliveira, Daniel A.; Leão, Rosa M. M.; Souza e Silva, Edmundo de (Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC); 2024: Anais do XLII Simpósio Brasileiro de Redes de Computadores e Sistemas DistribuídosDO - 10.5753/sbrc.2024.1470, 2024)
We study the metric Page Load Time (PLT) which has a significant impact on user experience, search engine optimization, and conversion rates. We explore how page complexity metrics, specifically content and infrastructure, affect PLT. We employ both supervised and unsupervised machine learning models to analyze the influence of these metrics at multiple levels: single page, page category, cluster, and general. Our study shows that the number of bytes, requests, and distinct images are key features in PLT prediction, with the page category model generally outperforming others. The results contribute to a better understanding of the factors influencing PLT and show some insights into how to optimize web pages for better user experiences and business outcomes.