Abstract
The cross-border intelligent marketing algorithm based on traditional linear models is relatively single in information feature extraction, making it difficult to effectively handle complex scenarios containing a large amount of implicit information in users and markets, resulting in poor personalized marketing effectiveness. To address this issue, this article proposes a cross-border intelligent marketing model that integrates rating information and user labels using a multi-layer perceptron grey wolf optimization and convolutional neural network (MLP-GWO-CNN). This model extracts implicit high-order information through nonlinear methods and can handle complex and sparse marketing data. Firstly, a dual path deep network structure was designed, in which one path was modeled using a multi-layer perceptron (MLP) to extract user interest features based on historical interaction ratings; Another path utilizes Convolutional Neural Networks (CNN) to extract semantic features from user label information and construct item feature representations. In response to the sensitivity of MLP algorithm to initial values and its tendency to fall into local optima, this paper uses GWO algorithm to optimize MLP. Next, the latent feature vectors generated by MLP and CNN are fused in the output layer to generate the final predictive marketing strategy last. Experiments were conducted using a real cross-border e-commerce dataset, and the results showed that compared with traditional recommendation algorithms, the MLP-GWO-CNN model proposed in this paper performs better in utilizing user tag information, effectively improving the accuracy and personalization of marketing recommendations. The accuracy of the model is over 89%, and the recall rate is over 90%.