Artificial intelligence regional cross-border e-commerce shopping guide platform information personalized recommendation algorithm

Abstract

In current period, the usage of cross-border e-commerce platforms has become more convenient thanks to internet information technology and artificial intelligence technology, and recommendations are accurate thanks to the use of current technologies to offer customers individualized recommendations. Customer's satisfaction with platforms for cross-border e-commerce shopping guides might be easily decreased due to their low degree of recommendation accuracy. Through experiments, it is determined that the improved collaborative filtering algorithm is applied to artificial intelligence across regional platforms for cross-border e-commerce shopping guides. The collaborative filtering algorithm is combined in this paper to investigate the application of the improved SU-SICF algorithm in the information personalized recommendation of the artificial intelligence regional cross-border e-commerce shopping guide platform. The testing results have been determined that the enhanced collaborative filtering algorithm is used throughout Information on the e-commerce shopping guide platform is superior when it comes to MAE value, accuracy rate, recall rate, and F value.