Personalized recommendation of e-commerce based on big data technology

With the rapid development of e-commerce, personalized recommendation system has become a key technology to enhance user experience and business value. In this paper, a personalized recommendation solution based on incremental data update model and adaptive collaborative filtering algorithm is proposed to solve the problems of sparse data, dynamic change of user interest and lack of real-time performance faced by traditional recommendation systems. By constructing the three-layer architecture of "User behavior record - incremental update model - Personalized recommendation algorithm", the system realizes dynamic feature optimization and real-time decision making. In the construction of the model, the difference between the user and the project is calculated by introducing the difference matrix, and the local parameter is updated efficiently by combining the incremental learning mechanism. At the algorithm level, an adaptive collaborative filtering framework is designed to integrate multi-source heterogeneous data to generate personalized recommendation strategies. The experimental results show that the applicability of the proposed method is 90.0%, and the prediction accuracy is stable above 90%, which is significantly better than the deep neural network and graph embedding model. The study further takes e-commerce logistics services as a scenario to verify the effectiveness of the system in dynamic demand matching, real-time behavior perception and multidimensional data governance, providing theoretical support and practical paths for optimizing recommendation logic, balancing accuracy and diversity of e-commerce platforms.