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Table 6 MAE comparison among various active learning approaches in PMF (A smaller MAE means a better performance)

From: An online-updating algorithm on probabilistic matrix factorization with active learning for task recommendation in crowdsourcing systems

 

PMF with active learning approaches

No. of selected samples

T[N]W[Reli]+ T[MaxDiff]W[Reli] (ActivePMFv1)

T[MaxDiff]W[Reli]

T[MaxDiff]W[Rand]

T[Rand]W[Reli]

T[Rand]W[Rand]

1000

0.3658

0.4731

0.4728

0.4179

0.3607

2000

0.2778

0.4579

0.4592

0.3580

0.2739

3000

0.1955

0.4567

0.4567

0.3083

0.2340

4000

0.1473

0.4325

0.4371

0.2642

0.2090

5000

0.1151

0.4089

0.4122

0.2296

0.1866

6000

0.0977

0.2427

0.3887

0.1941

0.1677

7000

0.0945

0.1872

0.2646

0.1716

0.1435

8000

0.0899

0.1334

0.1908

0.1447

0.1122

9000

0.0652

0.0898

0.1214

0.0994

0.0728

10000

0.0334

0.0357

0.0583

0.0394

0.0406

11000

0.0176

0.0247

0.0312

0.0287

0.0388