Efficacy Improvement of Convolutional Neural Network-based Artificial Intelligence Recommendation Model in Cross-border E-commerce Marketing

This paper optimizes the K-means clustering algorithm based on the RFM model improved by the entropy weight method and then using the distribution between the samples, and adopts the combination of both density and distance to accurately classify the cross-border e-commerce customers.Finally, the capsule network recommendation model is used as the benchmark model, and the CCN4SR model is designed to accurately recommend goods to customers.The results show that cross-border e-commerce customers are categorized into five-star to one-star customer groups, which focus on "return on investment, pursuit of social value, the pursuit of cost-effective, the pursuit of low prices, while having their own different consumer preferences".The capsule network outperforms CNN on both training and test sets, and its precision, recall and F1 value are above 92% on the test set, which shows that the capsule network is well adapted in the ield of implicit feedback recommendation.