人工智能驱动电商个性化推荐系统的优化路径——基于用户隐私保护视角

Optimization Paths for AI-Driven Personalized Recommendation Systems in E-Commerce—From the Perspective of User Privacy Protection

摘要: 随着人工智能技术的快速发展，个性化推荐系统已成为电商平台提升用户体验和商业转化效率的重要技术手段。然而，推荐系统在高度依赖用户数据的同时，也加剧了用户隐私泄露风险，引发了数据滥用、算法歧视和信息不对称等一系列问题。在数据保护法律法规日益完善和公众隐私意识不断增强的背景下，个性化推荐效果与用户隐私保护之间的张力愈发凸显，如何在隐私约束条件下实现推荐系统的持续优化，成为电商平台和学界共同关注的重要议题。本文从用户隐私保护视角出发，系统探讨人工智能驱动电商个性化推荐系统的优化路径。

Abstract: With the rapid development of artificial intelligence technology, personalized recommendation systems have become an important technical means for e-commerce platforms to enhance user experience and improve commercial conversion efficiency. However, while relying heavily on user data, these systems have also exacerbated the risks of user privacy leakage, giving rise to a series of issues such as data abuse, algorithmic discrimination, and information asymmetry. Against the backdrop of the increasing improvement of data protection laws and regulations and the growing public awareness of privacy, the tension between the effectiveness of personalized recommendations and user privacy protection has become increasingly prominent. How to achieve the continuous optimization of recommendation systems under privacy constraints has emerged as a key research topic of common concern to both e-commerce platforms and academic circles. From the perspective of user privacy protection, this paper systematically explores the optimization paths of artificial intelligence-driven personalized recommendation systems in e-commerce.