Recommender Systems (RSs) can be found in many modern applications and that expose the user to a huge collections of items
and helps user to decide on appropriate items, and ease the task of finding preferred items in the collection. Recently Recommender systems
are gaining popularity in both commercial and research community, where many algorithms have been used for providing
recommendations. There are many evaluation metrics used for comparing this recommendation algorithms. The literature on recommender
system evaluation offers a large variety of evaluation metrics and choose most appropriate evaluation metric among them. Error-based
metrics likes MAE, RMSE and Ranking-based metrics like Precision and Recall are discussed in this paper. Using this evaluation metrics
evaluate the performance of recommender systems.