Evaluation Measures  Equations  Description
Precision  Precision=1n ∑j|Rj ∩Mj ||Rj| (3)  This is defined as the ratio of the total number of items appearing in both Rj and Mj to the total number of Rj [66]. n is the total number of users. A higher value for the Precision means better performance and higher accuracy.
Recall  Recall=1n ∑j|Rj ∩Mj ||Mj| (4)  The Recall measure is defined as the ratio of the total number of items appearing in both Rj and Mj to the number of Mj [66]. Similarly, to the Precision measure, a higher value for Recall means a better performance and higher accuracy for the recommender algorithm.
F-Score  F Score = 2*(Recall * Precision)/(Recall + Precision) (5)  The F-score combines both the precision and recall into one metric that captures both properties. In other words, it is the weighted average for precision and recall. This metric gives an overview of the model results.