The evaluation of RSs has been, and still is, the object of active research in the field. Since the advent of the first RS, recommendation performance has been usually equated to the accuracy of rating prediction, that is, estimated ratings are compared against actual ratings, and differences between them are computed by means of the MAE and RMSE metrics. In terms of the effective utility of recommendations for users, there is, however, an increasing realization that the quality (precision) of a ranking of recommended items can be more important than the accuracy in predicting specific rating values. As a result, precision-oriented metrics are being increasingly considered in the field, and a large amount of recent work has focused on evaluating top-N ranked recommendation lists with the above type of metrics.