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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.08959 |
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| _version_ | 1866911052644483072 |
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| author | Li, Xiang Wang, Xinyu Lin, Yifan |
| author_facet | Li, Xiang Wang, Xinyu Lin, Yifan |
| contents | In order to improve the accuracy of cross-platform advertisement recommendation, a graph neural network (GNN)- based advertisement recommendation method is analyzed. Through multi-dimensional modeling, user behavior data (e.g., click frequency, active duration) reveal temporal patterns of interest evolution, ad content (e.g., type, tag, duration) influences semantic preferences, and platform features (e.g., device type, usage context) shape the environment where interest transitions occur. These factors jointly enable the GNN to capture the latent pathways of user interest migration across platforms. The experimental results are based on the datasets of three platforms, and Platform B reaches 0.937 in AUC value, which is the best performance. Platform A and Platform C showed a slight decrease in precision and recall with uneven distribution of ad labels. By adjusting the hyperparameters such as learning rate, batch size and embedding dimension, the adaptability and robustness of the model in heterogeneous data are further improved. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08959 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network Enhanced Sequential Recommendation Method for Cross-Platform Ad Campaign Li, Xiang Wang, Xinyu Lin, Yifan Machine Learning In order to improve the accuracy of cross-platform advertisement recommendation, a graph neural network (GNN)- based advertisement recommendation method is analyzed. Through multi-dimensional modeling, user behavior data (e.g., click frequency, active duration) reveal temporal patterns of interest evolution, ad content (e.g., type, tag, duration) influences semantic preferences, and platform features (e.g., device type, usage context) shape the environment where interest transitions occur. These factors jointly enable the GNN to capture the latent pathways of user interest migration across platforms. The experimental results are based on the datasets of three platforms, and Platform B reaches 0.937 in AUC value, which is the best performance. Platform A and Platform C showed a slight decrease in precision and recall with uneven distribution of ad labels. By adjusting the hyperparameters such as learning rate, batch size and embedding dimension, the adaptability and robustness of the model in heterogeneous data are further improved. |
| title | Graph Neural Network Enhanced Sequential Recommendation Method for Cross-Platform Ad Campaign |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.08959 |