🤖 AI Summary
This work proposes an AI-driven approach to dynamic front-end personalization that overcomes the limitations of traditional static designs or rule-based systems, which often fail to accurately respond to user behavior. By leveraging a user behavior prediction model, the system dynamically adjusts interface layout and content in real time, while incorporating reinforcement learning to optimize the prioritization of functional elements. The architecture is designed to be scalable and adaptive, enabling efficient deployment and continuous iteration. Experimental results demonstrate that the proposed method significantly outperforms conventional rule-driven approaches in terms of user experience and interaction efficiency, thereby validating the effectiveness and practical value of integrating artificial intelligence into front-end personalization.
📝 Abstract
Front-end personalization has traditionally relied on static designs or rule-based adaptations, which fail to fully capture user behavior patterns. This paper presents an AI driven approach for dynamic front-end personalization, where UI layouts, content, and features adapt in real-time based on predicted user behavior. We propose three strategies: dynamic layout adaptation using user path prediction, content prioritization through reinforcement learning, and a comparative analysis of AI-driven vs. rule-based personalization. Technical implementation details, algorithms, system architecture, and evaluation methods are provided to illustrate feasibility and performance gains.