Automatic Recommendation Algorithm for E-Commerce Personalized Information Based on B2C Data Model Analysis

In the digital age, people begin to use computers and multimedia technology to obtain a large amount of information and transform it into valuable content. As an emerging industry, e-commerce has become a trend. The personalized recommendation system is one of the essential links to achieve this goal. It can help users quickly and accurately find relevant product information or services that they are interested in, like and need to know about, thereby improving the efficiency and economic benefits of enterprises. This article conducts in-depth research on the automatic recommendation algorithm for e-commerce personalized information based on B2C data model, and designs a recommendation model based on B2C data model analysis. Afterwards, this article carried out simulation tests on the functions of the system. The test results show that the average time required for the system to run data is 146ms and 146.4ms; the average accuracy rate of browsing the product list is 97.6%, and the accuracy rate of displaying the product details page is 96.2%; the average matching accuracy is 97.2% and 98% respectively.