Table 1 Model evaluation scores for all datasets. First rank scores and AttentionDDI model scores are reported in bold

AttentionDDI\(\ddag\)

0.954

0.924

0.986

0.904

0.989

0.775

0.986

0.890

AttentionDDI (without siamese)\(\ddag\)

0.944

0.907

0.965

0.791

0.945

0.277

0.907

0.443

AttentionDDI (without Attention & siamese)\(\ddag\)

0.944

0.909

0.926

0.596

0.962

0.491

0.953

0.639

NDD*

0.954

0.922

0.994

0.890

0.994

0.830

0.992

0.947

 Classifier ensemble*

0.956

0.928

0.936

0.487

0.990

0.541

0.986

0.756

 Weighted average ensemble*

0.948

0.919

0.646

0.440

0.695

0.484

0.974

0.599

 RF*

0.830

0.693

0.982

0.812

0.737

0.092

0.889

0.167

 LR*

0.941

0.905

0.911

0.251

0.977

0.487

0.916

0.472

 Adaptive boosting*

0.722

0.587

0.904

0.185

0.830

0.143

0.709

0.150

 LDA*

0.935

0.898

0.894

0.215

0.953

0.327

0.889

0.414

 QDA*

0.857

0.802

0.926

0.466

0.709

0.317

0.536

0.260

 KNN*

0.730

0.134

0.927

0.785

0.590

0.064

0.603

0.235

ISCMF\(\dag\)

0.899

0.864

–

–

0.898

0.767

0.898

0.792

 Classifier ensemble\(\dag\)

0.957

0.807

–

–

0.990

0.541

0.986

0.756

 Weighted average ensemble\(\dag\)

0.951

0.795

–

–

0.695

0.484

0.974

0.599

 Matrix perturbation\(\dag\)

0.948

0.782

–

–

–

–

–

–

 Neighbor recommender\(\dag\)

–

–

–

–

0.953

0.126

0.904

0.295

 Label propagation\(\dag\)

–

–

–

–

0.952

0.126

–

–

 Random walk\(\dag\)

–

–

–

–

–

–

0.895

0.181