Research on the Application and Efficiency Optimization of Artificial Intelligence in E-Commerce Recommendation System

人工智能作为数字经济发展的核心驱动力，正深刻重塑电商行业的运营模式。尽管当前电商推荐系统已取得显著成效，但仍面临冷启动、数据稀疏及隐私保护等诸多挑战。为突破电商推荐系统的效能瓶颈，实现商业价值与用户体验的双向提升，本研究通过理论分析与案例验证，从技术架构、算法模型到应用场景的拓展与优化，全方位提升系统效能。研究结果表明，借助多维度策略优化，能够显著提高推荐系统的精准度与用户体验。Artificial intelligence, as the core driving force of digital economic development, is profoundly reshaping the operational models of e-commerce. Despite significant achievements in current e-commerce recommendation systems, they still face numerous challenges such as cold start, data sparsity, and privacy protection. To break through the performance bottlenecks of e-commerce recommendation systems and achieve a dual improvement in commercial value and user experience, this study conducts theoretical analysis and case validation. It comprehensively enhances system efficiency from technical architecture, algorithm models to the expansion and optimization of application scenarios. The research findings indicate that leveraging multi-dimensional strategy optimization can significantly improve the accuracy and user experience of recommendation systems.