Research on personalized recommendation optimization of E-commerce system based on customer trade behaviour data

The mode that people consume on the shopping website gets more and more popular, it contributes the economic value in modern society. But it carries a series of problems to the consumers, such that the users can choose the expected product hardly. The paper mainly presents the fuzzy theory to deal with the users' behaviour data, and the trade comment text data is mainly classified by semantic quantitative analysis. A whole personalized recommendation model is built, and proposed evaluation and optimization function are applied to improve the accuracy of recommendation system with case study. Finally, the customer satisfaction function is better verified by radar chart.