Enhancing Web User Experience Using AI-based Personalization Techniques

Abstract

Enhancing user experience on the web has become a critical challenge due to the exponential growth of online content and the diversity of user preferences. Artificial Intelligence (AI)-based personalization techniques have emerged as an effective solution to deliver tailored content, improve usability, and increase user engagement. This study investigates the integration of AI-driven models—such as collaborative filtering, neural networks, and attention-based mechanisms—into web personalization systems. A comparative analysis demonstrates that advanced hybrid models, particularly those combining Neural Collaborative Filtering (NCF) with attention mechanisms, achieve superior performance in predicting user preferences and sustaining engagement. The experimental results highlight significant improvements in accuracy, session duration, and user satisfaction, compared to traditional recommendation methods. The findings underscore the potential of AI-based personalization to redefine web user experience and provide practical insights for implementing adaptive web platforms.