基于超图卷积神经网络的多行为感知服务推荐方法
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陆佳炜,李端倪,王策策,徐俊,肖刚
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Multi-behavior aware service recommendation based on hypergraph graph convolution neural network
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Jia-wei LU,Duan-ni LI,Ce-ce WANG,Jun XU,Gang XIAO
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表 2 MBSRHCNN模型与基线方法的对比实验结果 |
Tab.2 Accuracy and correlation comparison results of MBSRHCNN model and baseline method |
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模型 | 稀疏数据集 | | 稠密数据集 | HR@5 | NDCG@5 | HR@10 | NDCG@10 | MRR | HR@5 | NDCG@5 | HR@10 | NDCG@10 | MRR | AFMRec | 0.381 | 0.358 | 0.384 | 0.352 | 0.372 | | 0.412 | 0.407 | 0.428 | 0.404 | 0.383 | WR-MSN | 0.335 | 0.312 | 0.321 | 0.303 | 0.314 | 0.465 | 0.452 | 0.442 | 0.431 | 0.428 | DHCN | 0.342 | 0.303 | 0.352 | 0.323 | 0.328 | 0.383 | 0.336 | 0.371 | 0.342 | 0.334 | HyperRec | 0.481 | 0.474 | 0.513 | 0.489 | 0.452 | 0.512 | 0.498 | 0.527 | 0.515 | 0.482 | MBHT | 0.482 | 0.436 | 0.509 | 0.462 | 0.474 | 0.528 | 0.477 | 0.542 | 0.496 | 0.506 | MB-GCN | 0.387 | 0.322 | 0.419 | 0.349 | 0.364 | 0.363 | 0.311 | 0.392 | 0.327 | 0.339 | MBSRHGNN | 0.504 | 0.489 | 0.547 | 0.507 | 0.497 | 0.572 | 0.542 | 0.596 | 0.556 | 0.541 | IMP/% | 4.5 | 3.2 | 4.8 | 3.8 | 4.9 | | 8.3 | 8.8 | 5.6 | 7.9 | 7.8 |
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