Web performansının dinamik içerik optimizasyonu için çok silahlı hayvanlarla güçlendirilmiş öğrenim

The field of web performance optimization faces the challenge of dynamically optimizing web content to enhance user experience and maximize engagement. Traditional approaches to content optimization, such as static A/B testing or rule-based algorithms, often fall short in adapting to the dynamic nature of web content and fail to provide personalized experiences that align with user preferences. As a result, organizations struggle to achieve optimal web performance, leading to lower user satisfaction, decreased retention rates, and missed conversion opportunities. Additionally, the rapid growth of internet usage and the increasing demand for fast and efficient web experiences further intensify the need for effective content optimization strategies. Websites must continuously evaluate and adjust their content configuration to deliver engaging experiences that meet user expectations. However, manually identifying the most rewarding content variants in real-time becomes a daunting task as the number of potential combinations increases. Hence, there is a pressing need for an intelligent and data-driven approach that can dynamically optimize web content to maximize user engagement and improve web performance. this research aims to explore the application of reinforcement learning techniques with multi-armed bandits for dynamic content optimization of web performance.