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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