Prediction Model for E-Commerce User Purchase Behavior Based on Data Mining

A prediction model of e-commerce user purchase behavior based on data mining techniques is essential for enhancing marketing strategies and customer satisfaction in online platforms. To understand and forecast customer behavior, we used various data mining methodologies to establish the model. Firstly, for customer online shopping behavior patterns, we gathered data from multiple sources, including customer profiles, transaction histories, and browsing logs. Subsequently, we applied preprocessing techniques to refine and prepare the data for analysis. Knowledge storage and representation techniques were employed to organize and structure the extracted insights effectively. In the prediction model, subsystems for customer behavior pattern knowledge acquisition and knowledge service were included. Additionally, the FP-Growth algorithm was used to predict online shopping behavior based on the knowledge extracted from transactional data. By integrating these components, the prediction model predicts customer preferences to tailor marketing strategies. The model helps the growth and competitiveness in e-commerce.