GNN:Session-based Recommendation with Graph Neural Networks简介
Session-based Recommendation with Graph Neural Networks 摘要 作者提出SR-GNN,将session序列建模为图结构数据。在session图的基础上，GNN可以捕捉到items的复杂转换。每一个session利用注意力机制将整体偏好与当前偏好结合进行表示。 介绍 作者diss了 基于马尔可夫链的工作非常依赖数据独立性的假设的。 利用RNN的基
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相关文章
- 1. Graph Neural Networks for Social Recommendation
- 2. Memory Augmented Graph Neural Networks for Sequential Recommendation翻译
- 3. Few-shot Learning with Graph Neural Networks
- 4. Few-Shot Learning with Graph Neural Networks
- 5. Overlapping Community Detection with Graph Neural Networks
- 6. Structural Image Classification with Graph Neural Networks
- 7. IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS
- 8. 【SR-GNN】Session-based Recommendation with Graph Neural Networks详解
- 9. Graph-based Dependency Parsing with Graph Neural Networks
- 10. Sequential Recommendation with User Memory Networks简介