A rough set-based consumer buying behaviour prediction method in online marketing system

Aiming at the problems of large prediction deviation and low acquisition accuracy of consumer purchase behaviour in traditional online marketing systems, a rough set-based consumer purchase behaviour prediction method in online marketing system is proposed. By improving the accuracy and recall rate of online consumer buying behaviour prediction methods, the deviation of prediction results is reduced. The data of consumer purchase behaviour in the region related to rough set are reduced to improve the accuracy and recall rate, and the forecast bias is reduced by removing redundant features in the e-marketing system. With the rough set theory, the dimension of consumer behaviour vector is reduced, and a predictive model framework is built. The simulation results show that the accuracy and recall rate of this proposed method are higher than 95%, and the minimum deviation of the prediction result is only 8.12%, which proves that the prediction result is more reliable.