## 3 The Proposed Algorithm
INPUT: R is the initial User-Item Rating Database; thrNN and thrsim are the thresholds for the minimum number of NNs contributing to the formulation of a prediction and their cumulative similarity, respectively, used in the prediction robustness evaluation criteria. OUTPUT: An enriched version of the User-Item Rating Database, including the derived ratings */ DR_set = 0/* DR_set is the set of dynamic ratings to be added to the ratings matrix */ FOREACH user U  FOREACH item i  IF (rU, i == NULL) THEN /* User U has not rated item i; predict the rating according to Equation (3) */  rating_prediction = \(\overline{r_{U}}+\frac{\sum_{v\in\text{{\scriptsize{\rm{\rm{\rm{\rm{\rm{\rm{\rm{ \rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\rm{\



assess the CFDR algorithm performance in terms of density enrichment, rating prediction coverage and accuracy increase. In order to quantify the rating prediction accuracy, both the mean absolute error (MAE) and the root mean squared error (RMSE) [1,2] error metrics have been employed. We use both these metrics to obtain more comprehensive insight into the effect of each parameter value setting combination on the rating prediction accuracy, since, while the MAE metric accounts uniformly for all error magnitudes, the RMSE error metric penalizes larger error values more severely. All our experiments were executed on seven datasets. Six of these datasets have been sourced from Amazon [51,52], while the remaining one has been obtained from MovieLens [53]. In regard to the six Amazon datasets, in this work, we have used the 5-core ones (each of the users and items included in the dataset have at least 5 ratings), due to the fact that in 5-25% of the cases, the initial dataset contained only one rating for the respective item, meaning that when this rating was hidden it was impossible to compute a rating prediction for this item. Clearly, such an inability to compute predictions is not due to any algorithm deficiency, and accounting for them in the evaluation procedure lead to underestimation of the algorithm's coverage score. However, when using the respective five-core datasets, the prediction coverage of a CF algorithm could possibly reach the optimal value of 100%, making these datasets appropriate for evaluation. The six five-core Amazon datasets are relatively sparse (their densities, computed as \(\frac{\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#} \text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#} \text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{ \#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#} \text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#} \text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{ \#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#} \text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#} \text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#} \text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#} \text{\#}\text{\#}\text{\#}\text{\#}\text{\#}\text{\#}

Subsequently, the algorithm hyperparameter tuning experiments, described in Section 4.1 were executed. In these experiments, the training portion was used to compute the user similarities and the NN sets for the baseline algorithm (plain CF algorithm) as well as the DR-enriched user-item rating matrix for the CF\({}_{\text{DR}}\) algorithm. In this experiment, accuracy was measured by comparing the actual ratings in the validation portion of the dataset against the corresponding ratings that were computed by the two algorithms (DR-enriched under different hyperparameter settings, as well as baseline). The prediction coverage was computed as the number of unknown ratings for which rating predictions could be computed to the total number of unknown ratings.