Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment
Abstract E-commerce platforms have amassed extensive transaction data, which serves as a valuable source for price prediction. However, the diversity of commodities poses challenges such as data imbalance, model overfitting, and underfitting. To address these issues, this paper presents an improved...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-10767-8 |
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| author | Abuduaini Abudureheman Yan Zhao Aishanjiang Nilupaer |
| author_facet | Abuduaini Abudureheman Yan Zhao Aishanjiang Nilupaer |
| author_sort | Abuduaini Abudureheman |
| collection | DOAJ |
| description | Abstract E-commerce platforms have amassed extensive transaction data, which serves as a valuable source for price prediction. However, the diversity of commodities poses challenges such as data imbalance, model overfitting, and underfitting. To address these issues, this paper presents an improved generative adversarial network model that integrates the strengths of Conditional Generative Adversarial Nets and the Wasserstein Generative Adversarial Network. By introducing Wasserstein scatter and removing Lipschitz constraints, we propose the CWGAN model to mitigate data imbalance and enhance the quality of generated samples. Furthermore, we incorporate Adaptive Weight Adjustment (AWA) and a differential evolution strategy, resulting in the Adaptive Weight Adjustment-Conditional Wasserstein Generative Adversarial Network (AWA-CWGAN) algorithm. This algorithm employs a neighborhood learning strategy to update the optimal individuals within subpopulations, thereby reinforcing the influence of elite individuals during population evolution. Additionally, dynamic weight adjustment based on sparsity is implemented to increase genetic diversity within the population. Experimental results demonstrate that the AWA-CWGAN algorithm achieves complete convergence with only 16–25% of the global evolutionary generations required by the standard differential evolutionary algorithm or the hybrid frog-leaping algorithm. Moreover, the AWA-CWGAN algorithm surpasses baseline methods in accuracy (88.8%), precision (88.81%), recall (89.255%), and F1 score (87.95%). These results indicate that the proposed approach significantly enhances the accuracy of e-commerce product price predictions, providing robust decision-making support for merchants. |
| format | Article |
| id | doaj-art-980ba1d2ea4341a28af768d5f64ad23a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-980ba1d2ea4341a28af768d5f64ad23a2025-08-20T03:45:52ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-10767-8Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustmentAbuduaini Abudureheman0Yan Zhao1Aishanjiang Nilupaer2School of Economics, Guangdong University of Finance and EconomicsSchool of Public Finance and Taxation of GDUFE, Guangdong University of Finance and EconomicsSchool of Information Management, Xinjiang University of Finance and EconomicsAbstract E-commerce platforms have amassed extensive transaction data, which serves as a valuable source for price prediction. However, the diversity of commodities poses challenges such as data imbalance, model overfitting, and underfitting. To address these issues, this paper presents an improved generative adversarial network model that integrates the strengths of Conditional Generative Adversarial Nets and the Wasserstein Generative Adversarial Network. By introducing Wasserstein scatter and removing Lipschitz constraints, we propose the CWGAN model to mitigate data imbalance and enhance the quality of generated samples. Furthermore, we incorporate Adaptive Weight Adjustment (AWA) and a differential evolution strategy, resulting in the Adaptive Weight Adjustment-Conditional Wasserstein Generative Adversarial Network (AWA-CWGAN) algorithm. This algorithm employs a neighborhood learning strategy to update the optimal individuals within subpopulations, thereby reinforcing the influence of elite individuals during population evolution. Additionally, dynamic weight adjustment based on sparsity is implemented to increase genetic diversity within the population. Experimental results demonstrate that the AWA-CWGAN algorithm achieves complete convergence with only 16–25% of the global evolutionary generations required by the standard differential evolutionary algorithm or the hybrid frog-leaping algorithm. Moreover, the AWA-CWGAN algorithm surpasses baseline methods in accuracy (88.8%), precision (88.81%), recall (89.255%), and F1 score (87.95%). These results indicate that the proposed approach significantly enhances the accuracy of e-commerce product price predictions, providing robust decision-making support for merchants.https://doi.org/10.1038/s41598-025-10767-8Generative adversarial networksAdaptive weight adjustmentDifferential evolutionPrice prediction |
| spellingShingle | Abuduaini Abudureheman Yan Zhao Aishanjiang Nilupaer Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment Scientific Reports Generative adversarial networks Adaptive weight adjustment Differential evolution Price prediction |
| title | Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment |
| title_full | Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment |
| title_fullStr | Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment |
| title_full_unstemmed | Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment |
| title_short | Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment |
| title_sort | design of e commerce product price prediction model based on generative adversarial network with adaptive weight adjustment |
| topic | Generative adversarial networks Adaptive weight adjustment Differential evolution Price prediction |
| url | https://doi.org/10.1038/s41598-025-10767-8 |
| work_keys_str_mv | AT abuduainiabudureheman designofecommerceproductpricepredictionmodelbasedongenerativeadversarialnetworkwithadaptiveweightadjustment AT yanzhao designofecommerceproductpricepredictionmodelbasedongenerativeadversarialnetworkwithadaptiveweightadjustment AT aishanjiangnilupaer designofecommerceproductpricepredictionmodelbasedongenerativeadversarialnetworkwithadaptiveweightadjustment |