Research on the Operating Mechanism and Optimization Path of E-Commerce Algorithm Recommendation under the Platform Economy

在平台经济背景下，算法推荐已成为电商平台优化资源配置与提升交易效率的重要机制。本文基于信息不对称、注意力经济与长尾理论，对电商算法推荐的运行机制进行系统分析，揭示其在数据收集、算法匹配、反馈迭代与商业变现等环节的内在逻辑。在此基础上，构建平台与消费者之间的简化博弈模型，分析不同推荐策略下平台收益与用户福利的变化。研究发现，算法推荐在提升匹配效率的同时，也可能引发信息茧房、消费诱导、算法不透明及数据隐私风险等问题。进一步分析表明，过度商业导向的推荐策略虽有利于短期收益，但会削弱用户信任并影响平台长期发展。因此，应从提升算法透明度、增强推荐多样性、强化用户控制权、完善数据保护机制及平衡商业目标与用户利益等方面优化推荐系统。本文为理解电商算法推荐的运行机制与治理路径提供了参考。In the context of the platform economy, algorithmic recommendations have become a key mechanism for e-commerce platforms to optimize resource allocation and improve transaction efficiency. Based on information asymmetry theory, attention economy theory, and long-tail theory, this paper systematically analyzes the operating mechanism of e-commerce algorithmic recommendation, revealing its internal logic across four dimensions: data collection, algorithm matching, feedback iteration, and commercial monetization. Furthermore, a simplified game model between platforms and consumers is constructed to examine the changes in platform revenue and user welfare under different recommendation strategies. The results show that while algorithmic recommendations enhance matching efficiency, they may also lead to issues such as information cocoons, consumption manipulation, algorithmic opacity, and data privacy risks. Further analysis indicates that although overly commercially oriented recommendation strategies can increase short-term revenue, they may weaken user trust and adversely affect long-term platform development. Therefore, optimization should focus on improving algorithmic transparency, enhancing recommendation diversity, strengthening user control, refining data protection mechanisms, and balancing commercial objectives with user interests. This study provides a useful reference for understanding the operating mechanism and governance of algorithmic recommendations in e-commerce platforms.