Identification of User Preferences and Personalized Recommendation Strategy in E-commerce Platforms

With the rapid development of e-commerce platforms, accurately identifying user preferences and providing personalized recommendations has become a key strategy to enhance user satisfaction and platform market competitiveness. This article starts with the definition of user preference recognition, analyzes the core challenges faced by the e-commerce field, such as data complexity, differences in user behavior, privacy and data confidentiality issues, and potential biases in recommendation algorithms. It also strategically explores how to improve the quality of personalized recommendations. By using big data analysis technology to accurately capture user preferences, strengthen privacy and security protection, optimize the fairness and transparency of recommendation algorithms, and improve user interaction feedback mechanisms, specific improvement measures are aimed at providing practical suggestions for the optimization of personalized recommendation systems in future e-commerce.