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Table 7 RMSE 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 0.7101 0.8407 0.8396 0.7745 0.6715
2000 0.6097 0.8417 0.8417 0.7112 0.5355
3000 0.5110 0.8423 0.8420 0.6578 0.4833
4000 0.4164 0.8017 0.8089 0.6052 0.4533
5000 0.3180 0.7597 0.7655 0.5643 0.4247
6000 0.2621 0.6918 0.7245 0.5191 0.3978
7000 0.2566 0.6067 0.6150 0.4501 0.3605
8000 0.2563 0.5186 0.5381 0.3205 0.2991
9000 0.2054 0.3899 0.4844 0.2952 0.2210
10000 0.1259 0.1683 0.2139 0.1843 0.1524
11000 0.0880 0.1245 0.1357 0.1342 0.1396