## 5 Experiments
### Experimental Procedures, Evaluation, and Discussion
#### 5.4.1 Type 1: Experiments of Regular Users  
Experiment 1: Determining the K-NN and Minimum Similarity  
The proposed algorithm contains essential parameters: the active user's initial nearest neighbors in each genre (K-NN) and the final nearest neighbors for the active user in all genres (NN/). We tested selecting the optimal K-NN and minimum similarity (Avr. min. sim. ), i.e., the similarity threshold between an active user and their _NN/_f_ list, to obtain the optimal performance and examine changes to the accuracy of the proposed algorithm. Thus, different values of K-NN using UGSM, PCC, and COS, from 20 to 100 in increments of 5 per stage for both datasets were determined. We measured the algorithms' accuracies according to MAE. Figure 4 shows the results of the ML-20M dataset. It can be seen that the accuracy of the UGSM prediction was better when \(k=5\) and where MAE = 0.54. Therefore, in the rest of the experiments, the value of \(k\) was fixed to 35. For comparison, PCC scored the lowest, MAE = 0.68, when \(k=60\), and COS recorded MAE = 0.637 when \(k=65\). In the UGSM, \"avr min sim\" was derived using the following equation:  
\[w\ avr.min.sim=100*\left(1-\frac{\max\left(avr.diff\left(a_{gj}\,u_{i,gj}\right) \right)}{\max r}\right)\Leftrightarrow u_{i}\ \in\ NNf_{a}\ \text{list} \tag{7}\]  
where \(NNf_{a}\) represents the final \(NN\) for a; \(w\ avr.min.sim\) is the weight of average minimum similarity between \(a\) and \(NNf_{a}\); \(i=1,2,\,\ldots,\,NNf_{a}\) and j = 1, 2 \(\ldots\) # of common \(g\) between \(a\) and \(NNf_{a}\). In this model, we assumed that the weight of \(NNf_{a}\) on \(kNN\) (\(w.NNf_{a}=(\#NNf_{a})/(\#\ kNN)\)) had a significant role in enhancing the prediction accuracy by finding the proper neighbors. Therefore, we chose the optimal value for similarity according to the lowest MAE value and a suitable size of \(NNf_{a}\). The details are shown in Table 8.  
From Table 8, it is notable that when \(k=35\), \(W.NNf_{a}\) is distinct and higher than the rest of the neighbors recorded when \(k\) was greater or less than 35. W avr min sim was more significant than the remaining values, here w avr min sim = 0.5, with it, the minimum MAE was recorded. PCC and COS had an avr min sim = 0.2 and 0.23, respectively, with their ideal neighbor sizes. In comparison, the UGSM model picked up suitable neighbors with a smaller neighborhood size. For the R2-yahoo music dataset, Figure 5 shows the results of the prediction accuracies of UGSM, PCC, and COS. The accuracy of UGSM prediction is better when \(k\) = 45 and where MAE = 0.56. Therefore, in the rest of the experiments, the value of \(k\) was fixed to 45. PCC had the lowest MAE = 0.63 when \(k\) = 65; however, COS had a MAE = 0.61 when \(k\) = 55. Table 9 shows the values of avr min sim for the R2-yahoo music dataset. From Table 9, it can be seen that when \(k\) = 45, the W. NNf is high, and w avr min sim = 0.54. PCC and COS had an avr min sim = 0.18 and 0.21, respectively, and a greater MAE from their typical neighbor sizes. By comparing UGSM to the PCC and COS similarity measures in both datasets, the results demonstrated that UGSM captured better similarity, with a smaller neighborhood size and a small MAE. This result is a simple indication of UGSM's ability to choose correct neighbors. evaluation metrics. When the \(k\) was the optimal value in all models, UGSM's accuracy was significantly better than PCC and COS. Firstly, in Figure 6a (1) the results confirmed that UGSM decreased the MAE to 14.0% against PCC and 10.0% against COS for the ML-20M dataset. Secondly, Figure 6a (2) showed the P.L. of UGSM--22.0% and 17.0% better than PCC and COS, respectively. Finally, Figure 6a (3), by applying the Rel L metric, showed results and confirmed UGSM's progress. It increased the Rel L between users by 14.0% and 11.0 % for PCC and COS. The same progress was obtained for the R2-yahoo music dataset. In the second part, we compared the UGSM with modern CF algorithms in CF-SV according to [PERSON] et al. (2019), the User-MRDC by [PERSON] et al.