MB-AGCL: multi-behavior adaptive graph contrast learning for recommendation
Abstract Graph Convolutional Networks (GCNs) have achieved remarkable success in recommendation systems by leveraging higher-order neighborhoods. In recent years, multi-behavior recommendation has addressed the challenges of data sparsity and cold start problems to some extent. However, the introduc...
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| Автори: | , , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Springer
2025-04-01
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| Серія: | Complex & Intelligent Systems |
| Предмети: | |
| Онлайн доступ: | https://doi.org/10.1007/s40747-025-01880-2 |
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