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联合深推荐基于位置的社交网络的框架

表1

数据集在Yelp性能比较。改善TCENR相比,每个方法显示在括号中。
模型 精度 均方误差 Pre@10 Rec@10
高通滤波器 0.8141 0.1800 0.5526 0.3699
(1.69%) (34.94%) (18.51%) (40.98%)
NMF 0.8222 0.1189 0.7851 0.3517
(0.69%) (1.51%) (-16.58 ) (48.28%)
Geo-SAGE 0.7995 0.1807 0.2912 0.4145
(3.55%) (35.19%) (124.89%) (25.81%)
LCARS 0.8142 0.1612 0.6408 0.5127
(1.68%) (27.36%) (2.2%) (1.72%)
NeuMF 0.8273 0.1421 0.6488 0.5586
(0.07%) (17.59%) (0.94%) (-6.64 )
速度 0.8239 0.1186 0.6406 0.5049
(0.49%) (1.26%) (2.23%) (3.29%)
DeepCoNN 0.8037 0.1454 0.5385 0.323
(3.01%) (19.46%) (21.62%) (64.46%)
TCENR 0.8279 0.1171 0.6549 0.5215
0.8273 0.1161 0.6655 0.4738
(0.07%) (-0.86 ) (-1.59 ) (10.07%)