## 3 The Proposed Algorithm
Therefore, by comparing the proposed algorithm with both the CF\({}_{\text{VNN}}\) and CF\({}_{\text{FOAF}}\) algorithms, we obtain a holistic view of the algorithm performance, covering both rating prediction aspects (coverage and accuracy). Figure 6 illustrates the results obtained regarding the prediction coverage increase, when the metric used to quantify similarity between users is the PCC. We can observe that the CF\({}_{\text{DR}}\) algorithm, presented in this paper, successfully increases the average coverage across all datasets by 36.52% against the baseline (the coverage increment is calculated as \(\frac{coverage(DR-enriched dataset)-coverage(baseline)}{coverage( baseline)}\)). On the individual dataset level, the coverage improvement ranges from 2.61%, for the MovieLens 100 K dataset, to 83.1%, for the Amazon \"Grocery and Gourmet Food\" dataset. Notably, the MovieLens 100 K is a dense dataset (its density is equal to 3.17%), consequently exhibiting very high coverage in its initial form (approximately equal to 94%), hence the coverage improvement margin is limited. The CF\({}_{\text{FOAF}}\) algorithm, introduced in [29], achieved an average coverage increase of 34.7%, while the CF\({}_{\text{VNN}}\) algorithm [27] achieved an average coverage increase of 30.1%. On the individual dataset level, the relative performance edge of the proposed algorithm against the CF\({}_{\text{FOAF}}\) algorithm, which was found to achieve higher prediction coverage than the CF\({}_{\text{VNN}}\) algorithm, ranges from 4.1%, for the Amazon \"Grocery and Gourmet Food\" dataset, to 8.1%, for the Amazon \"Movies & TV\" dataset, for the sparse (Amazon) datasets that were considered. The average performance edge, across all the sparse datasets, of the proposed algorithm against the CF\({}_{\text{FOAF}}\) algorithm was measured at 5.44%, while the respective edge against the CF\({}_{\text{VNN}}\) algorithm was measured at 25.9%. Regarding the dense dataset \"MovieLens 100 K\", the proposed algorithm was found to surpass the performance of the CF\({}_{\text{VNN}}\) algorithm by a margin of 0.47% in absolute figures, while, in terms of relative improvement, the edge of the proposed algorithm is quantified to 22%. Figure 7 illustrates the respective coverage increase results when CS is used to quantify similarity between users. We can observe that the CF\({}_{\text{DR}}\) algorithm, presented in this paper, achieves an increase of the average coverage across all datasets by 24.13% against the baseline. On the individual dataset level, the coverage improvement ranges from 1.59%, for the MovieLens 100 K dataset, to 61.12%, for the

\[\text{HM(s)}=2*\frac{normCovInc(s)\times normAccInc(s)}{normCovInc(s)+normAccInc (s)} \tag{6}\]2 Coverage increase for the datasets summarized in Table 4, when the PCC user similarity metric is used. Amazon \"Grocery and Gourmet Food\" dataset. Again, the smallest coverage increase is achieved in the case of the MovieLens 100 K, which, due to its high density and the corresponding high initial coverage, offers little coverage improvement potential. On the individual dataset level, the relative performance edge of the proposed algorithm against the CF\({}_{\text{FOAF}}\) algorithm, which was again found to achieve higher prediction coverage than the CF\({}_{\text{VNN}}\) algorithm, ranges from 1.3%, for the Amazon \"Grocery and Gourmet Food\" dataset, to 4.4%, for the Amazon \"Movies and TV\" dataset. The average performance edge, across all the sparse datasets, of the proposed algorithm against the CF\({}_{\text{FOAF}}\) algorithm was measured at 2.6%, while the respective edge against the CF\({}_{\text{VNN}}\) algorithm was measured at 14.8%. Overall, under the CS similarity metric, coverage improvement is lower, when compared to that observed under the PCC similarity metric. This is due to the fact that, under the CS metric, the baseline algorithm (plain CF) exhibits higher coverage (78.5% vs. 60.4% under the PCC), hence the margin for improvement is narrower.