SAGCN weights each graph-convolution layer's update by the distance between old and new embeddings and reports Recall/NDCG gains over the strongest baselines on four datasets, ranging from 0.08% to 6.19%.
Recommendation systems: Algorithms, challenges, metrics, and business opportunities.Applied Sciences, 10(21):7748
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Distance-aware Self-adaptive Graph Convolution for Fine-grained Hierarchical Recommendation
SAGCN weights each graph-convolution layer's update by the distance between old and new embeddings and reports Recall/NDCG gains over the strongest baselines on four datasets, ranging from 0.08% to 6.19%.