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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