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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