E-Commerce Online Shopping Platform Recommendation Model Based on Integrated Personalized Recommendation

Abstract

With the continuous innovation of Internet technology and the substantial improvement of network basic conditions, e-commerce has developed rapidly. Online shopping has become the mainstream mode of e-commerce. In order to solve the problem of information overload and information loss in the selection of e-commerce online shopping platform, a personalized recommendation system using information filtering technology has come into being. An e-commerce online shopping platform recommendation model is proposed based on integrated multiple personalized recommendation algorithms: random forest, gradient boosting decision tree, and eXtreme gradient boosting. The proposed model is tested on the public data set. The experimental results of the separate model and mixed model are compared and analyzed. The results show that the proposed model reduces the recommendation sparsity and improves the recommendation accuracy.