Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network
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 p...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89534-8 |
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| author | Zongping Lin Jing Yang Yabin Lian Yanhong Chen Zhonghui Huang Kexin Ning |
| author_facet | Zongping Lin Jing Yang Yabin Lian Yanhong Chen Zhonghui Huang Kexin Ning |
| author_sort | Zongping Lin |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-d3276b68b9b341fcb00758140be3c788 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d3276b68b9b341fcb00758140be3c7882025-08-20T02:48:30ZengNature PortfolioScientific Reports2045-23222025-02-0115111410.1038/s41598-025-89534-8Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural networkZongping Lin0Jing Yang1Yabin Lian2Yanhong Chen3Zhonghui Huang4Kexin Ning5School of Economics and Management, Quanzhou University of Information EngineeringSchool of Economics and Business, Xiamen City UniversityAcademy of Art & Design, Minnan Science and Technology CollegeAcademy of Art & Design, Minnan Science and Technology CollegeXiamen Meitu Mobile Technology Co., Ltd.School of Arts and Media, Beijing Normal UniversityAbstract 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%.https://doi.org/10.1038/s41598-025-89534-8Multilayer perceptronCross border intelligent marketing systemConvolutional neural networkSocial labelItem ratingGrey wolf optimization |
| spellingShingle | Zongping Lin Jing Yang Yabin Lian Yanhong Chen Zhonghui Huang Kexin Ning Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network Scientific Reports Multilayer perceptron Cross border intelligent marketing system Convolutional neural network Social label Item rating Grey wolf optimization |
| title | Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network |
| title_full | Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network |
| title_fullStr | Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network |
| title_full_unstemmed | Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network |
| title_short | Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network |
| title_sort | optimization design of cross border intelligent marketing management model based on multi layer perceptron grey wolf optimization convolutional neural network |
| topic | Multilayer perceptron Cross border intelligent marketing system Convolutional neural network Social label Item rating Grey wolf optimization |
| url | https://doi.org/10.1038/s41598-025-89534-8 |
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