GNN:Session-based Recommendation with Graph Neural Networks簡介
Session-based Recommendation with Graph Neural Networks 摘要 作者提出SR-GNN,將session序列建模爲圖結構數據。在session圖的基礎上，GNN可以捕捉到items的複雜轉換。每一個session利用注意力機制將整體偏好與當前偏好結合進行表示。 介紹 作者diss了 基於馬爾可夫鏈的工作非常依賴數據獨立性的假設的。 利用RNN的基
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相關文章
- 1. Graph Neural Networks for Social Recommendation
- 2. Memory Augmented Graph Neural Networks for Sequential Recommendation翻譯
- 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詳解
- 9. Graph-based Dependency Parsing with Graph Neural Networks
- 10. Sequential Recommendation with User Memory Networks簡介