Recommender systems research has used several types of measures for evaluating the quality of a recommender system. They can be mainly categorized into two classes:
The lower the MAE, the more accurately the recommendation engine predicts user ratings. Root Mean Squared Error (RMSE), and Correlation are also used as statistical accuracy metric
We used MAE as our choice of evaluation metric to report prediction experiments because it is most commonly used and easiest to interpret directly. In our previous experiments  we have seen that MAE and ROC provide the same ordering of different experimental schemes in terms of prediction quality.