电商个性化推荐算法的公平性与用户隐私保护平衡机制研究

Research on Fairness and User Privacy Protection Balancing Mechanisms in E-Commerce Personalised Recommendation Algorithms

摘要: 在大数据与人工智能深度赋能电子商务的背景下，个性化推荐算法已成为平台配置流量资源与塑造交易秩序的重要工具，但其在提升效率的同时不断引发公平性失范与用户隐私侵蚀等突出问题，相关风险由个别现象演变为系统性挑战。围绕算法偏向、标签固化、过度数据收集与平台中心化治理等现实情境，从公平性与隐私保护的内在张力出发，提出将多目标优化理念嵌入算法设计，并通过技术治理与制度规制协同、多主体责任分担等方式构建平衡机制，以推动电商推荐算法向效率与权利保障并重的方向演进。

Abstract: Against the backdrop of big data and artificial intelligence profoundly empowering e-commerce, personalised recommendation algorithms have become pivotal tools for platforms to allocate traffic resources and shape transactional order. Yet while enhancing efficiency, they continually raise prominent issues such as fairness violations and erosion of user privacy, with associated risks evolving from isolated incidents into systemic challenges. Addressing practical scenarios such as algorithmic bias, label entrenchment, excessive data collection, and platform-centralised governance, this paper proposes embedding multi-objective optimisation principles into algorithmic design. By synergising technical governance with institutional regulation and implementing multi-stakeholder responsibility sharing, a balancing mechanism can be established. This approach will propel e-commerce recommendation algorithms towards an evolution that equally prioritises efficiency and rights protection.