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Table 2 Evaluation metrics

From: Recommender systems to support learners’ Agency in a Learning Context: a systematic review

Measure Definition Equation N %
Precision The ratio of relevant items selected to the number of items selected. \( P=\frac{N_{rs}}{N_s} \) 20 66.67
Recall The ratio of relevant items selected to the total number of relevant items available. \( R=\frac{N_{rs}}{N_r} \) 20 66.67
F1 The combination of precision and recall measures, because it is possible to increase one at the expense of the other. \( {F}_1=\frac{2 PR}{P+R} \) 14 46.67
Accuracy rate Ratio of good recommendations to all recommendations. \( AcR=\frac{N_r}{N} \) 7 23.33
Mean absolute error The divergence between prediction and actual opinion. \( MAE=\frac{\sum \limits_{i=1}^n\left|{p}_i-{r}_i\right|}{N} \) 5 16.67
Coverage The ratio of items for which a recommender system can provide recommendations. \( Coverage=\frac{N_s}{N} \) 4 13.33
Root mean squared error Mean Squared Error (MSE) is a metric that penalizes more major errors than minor errors, but does not offer an intuitive scale. RMSE repositions MSE results along a more intuitive scale. \( RMSE=\sqrt{\frac{\sum \limits_{i=1}^n{\left({p}_i-{r}_i\right)}^2}{N}} \) 1 3.33
Average rating The average rating from all the users. \( AvR=\frac{\sum \limits_{i=1}^n{r}_i}{\# ratings} \) 1 3.33