Skip to main content

Table 2 Comparison of MAE and RMSE on the WS-DREAM dataset

From: Neighborhood-aware web service quality prediction using deep learning

Attributes Methods Matrix density = 5% Matrix density = 10% Matrix density = 15% Matrix density = 20%
   MAE RMSE MAE RMSE MAE RMSE MAE RMSE
RT UPCC 0.9553 2.1269 0.7823 1.8569 0.6716 1.7264 0.5972 1.7177
  IPCC 1.1026 2.2583 0.8780 1.9893 0.7840 1.8628 0.7223 1.7948
  UIPCC 0.8471 1.9208 0.7290 1.7308 0.6128 1.5906 0.5520 1.5878
  CMF 0.6116 1.4142 0.5169 1.3562 0.4917 1.2163 0.4591 1.1988
  NMF 0.6182 1.5746 0.6040 1.5494 0.5990 1.5345 0.5982 1.5331
  PMF 0.5678 1.4735 0.4996 1.2866 0.4720 1.2163 0.4492 1.1828
  NIMF 0.5514 1.4075 0.4854 1.2745 0.4534 1.1980 0.4357 1.1678
  NAMF 0.5384 1.3853 0.4850 1.2592 0.4529 1.2071 0.4350 1.1443
  NDL 0.4037 1.3596 0.3904 1.1686 0.3877 1.0924 0.3783 1.0793
TP UPCC 56.4816 95.4345 47.3569 78.1629 41.6976 70.9251 37.2768 67.9981
  IPCC 46.5634 79.6976 42.5893 73.5783 36.5033 68.4784 34.3576 65.4433
  UIPCC 40.0451 74.5033 36.4308 64.9208 33.8068 59.5171 29.2445 56.5301
  CMF 30.8275 69.2836 26.4586 59.3657 21.7944 52.9409 17.8588 47.1635
  NMF 25.7529 65.8517 17.8411 53.9896 15.8939 51.7322 15.2516 48.6330
  PMF 19.9034 54.0508 16.1755 46.4439 15.0956 43.7957 14.6694 42.4855
  NIMF 17.9297 52.6573 16.0542 46.9409 14.4346 43.1596 13.7099 41.1689
  NAMF 18.0837 52.8658 15.9808 46.9788 14.6661 43.0206 13.9386 40.7481
  NDL 16.2700 52.5069 14.9585 46.4381 14.0924 42.7671 13.1675 40.5656