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.

翻译：传统前端个性化通常依赖静态设计或基于规则的适配方法，这些方法难以全面捕捉用户行为模式。本文提出一种基于人工智能的动态前端个性化方案，通过预测用户行为实现用户界面布局、内容与功能的实时自适应。我们提出三项核心策略：基于用户路径预测的动态布局适配、通过强化学习实现的内容优先级排序，以及人工智能驱动与规则式个性化方案的对比分析。文中详细阐述了技术实施方案、算法原理、系统架构与评估方法，以论证该方案的可行性与性能提升效果。