## 3 The Proposed Algorithm
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \
\begin{tabular}{c} **Evaluation** \\ **Dataset(s)** \\ \end{tabular} & **Coverage Increase** & **Accuracy Increase** & **Comments** \\ \hline \multirow{4}{*}{HyCov [33]} & \multirow{4}{*}{MovieLens} & \multirow{4}{*}{2.07\%} & \multirow{4}{*}{MAE: 0.12\%} & The CF\({}_{\text{DR}}\) algorithm achieves higher coverage and accuracy improvements as measured using the MAE metric in the same dataset (2.66\% and 0.66\%, respectively, under a five-fold cross validation performance assessment procedure). \\ \cline{5-5}  & & & & The CF\({}_{\text{DR}}\) algorithm achieves higher coverage and accuracy improvements both for sparse datasets (avg. coverage increase: 37.17\%, avg. MAE improvement: 2.27\%; 1.56\%; RMSE: 2.33\% MovieLens: 0.46\%; RMSE: 0.56\% & avg. RMSE improvement: 3.04\%, under a hide-one performance assessment procedure) and the dense MovieLens dataset (2.54\%, 0.61\% and 0.67\%, respectively, again under a hide-one performance assessment procedure). \\ \hline \multirow{4}{*}{CF\({}_{\text{FOAF}}\)[29]} & \multirow{4}{*}{MovieLens \&} \multirow{4}{*}{avg. for sparse datasets: 35.45\%} & The CF\({}_{\text{DR}}\) algorithm achieves higher coverage and accuracy improvements both for sparse datasets (avg. coverage increase: 37.17\%, avg. MAE improvement: 2.27\%; avg. RMSE improvement: 3.04\%, under a hide-one performance assessment procedure) and the dense MovieLens dataset (2.54\%, 0.61\% and 0.67\%, respectively, again under a hide-one performance assessment procedure). \\ \cline{5-5}  & & & & The CF\({}_{\text{DR}}\) algorithm achieves higher coverage and accuracy improvements both for sparse datasets (avg. coverage increase: 37.17\%, avg. MAE improvement: 2.27\%; avg. RMSE improvement: 3.04\%, under a hide-one performance assessment procedure) and the dense MovieLens dataset (2.54\%, 0.61\% and 0.67\%, respectively, again under a hide-one performance assessment procedure). \\ \cline{5-5}  & & & & The CF\({}_{\text{DR}}\) algorithm achieves higher coverage and accuracy improvements both for sparse datasets (avg. coverage increase: 37.17\%, avg. MAE improvement: 2.27\%; avg. RMSE improvement: 3.04\%, under a hide-one performance assessment procedure) and the dense MovieLens dataset (2.54\%, 0.61\% and 0.67\%, respectively, again under a hide-one performance assessment procedure). \\ \cline{5-5}  & & & &
\begin{tabular}{c} CF\({}_{\text{DR}}\) algorithm achieves higher accuracy improvements than ANLF (MovieLens dataset RMSE improvement: 0.71\% under a five-fold cross validation performance assessment procedure). Note that, since the ANLF is matrix factorization based, a prediction is always formulated for every empty cell in the user-item rating matrix, with predictions involving users or items having very few ratings degenerating to a dataset-dependent constant value [40]. \\ \cline{5-5}  & & & & Despite this particularity, accuracy precisions are comparable because they are always computed considering solely known ratings, because only for these ratings the ground truth is known. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of CF\({}_{\text{DR}}\) with algorithms that exploit only the user-item rating matrix for achieving coverage increase. of a user's NNs take part in the prediction formulation. The experimental results indicated that the optimal significance of the derived ratings, created by the CF\({}_{\text{DR}}\) algorithm preprocessing step, was the exact same with the real user ratings. Therefore, no other complementary information needs to be stored in the user-item rating DB, except the DRs. The presented algorithm was experimentally verified using seven datasets. The evaluation results showed that the introduction of the derived ratings achieved to increase the datasets density by 92 times on average, when the PCC similarity metric is applied, and by 172 times on average, when the CS similarity metric is applied, for all the datasets tested.