# The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System
## 3 Results
### Evaluation of the Proposed Method Result  
This section shows the evaluation result of all the methods used in the experiment, such as the collaborative filtering method, maximal clique method and the proposed method. For example, in Table 7, \"u1\" is an ID of a 35 years old male new user, and he is an engineer by profession. Alternately, \"u80\" is an ID of a 24 years old female new user, and she is a student. The system recommended movie IDs to \"u1\" as m24, m242, m532, m821 and m1222, which refers to Toy Story (1995), Twelve Monkeys (1995), Dead Man Walking (1995), Antonia's Line (1995) and From Dusk Till Dawn (1996) movies, respectively. The mean absolute percentage error used for evaluating the output of the experiment, as shown in Figures 7 and 8. \begin{table}
\begin{tabular}{c c} \hline \hline
**\#user** & **Movie ID** \\ \hline u1 & M1, M2, M5, M80, M100 \\ u2 & M1, M23, M35, M50, M150 \\ u3 & M11, M23, M511, M830, M1200 \\ u4 & M211, M232, M675, M830, M1200 \\ u5 & M321, M434, M531, M830, M1100 \\ u6 & M14, M22, M53, M820, M1300 \\... & \\ u47 & M211, M232, M675, M830, M1200 \\ u48 & M321, M434, M531, M830, M1100 \\ u49 & M14, M22, M53, M820, M1300 \\ u50 & M321, M434, M531, M830, M1100 \\ u52 & M321, M434, M531, M830, M1100 \\ u54 & M31, M34, M531, M80, M1100 \\... &... \\ u143 & M241, M242, M532, M840 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: List of the recommended movies. Figure 7: Mean absolute percentage error (MAPE) results using the proposed method. Figure 7 shows the result of the proposed method, which displays various values depending on the value of k. The value of k is the number of users in each community. In essence, the community was detected from the FCA network graph with the help of the social network analysis methodology, such as the \(k\)-clique method. The number of users in each community begins from the 3 people until the maximum number of users in the community, which can be detected by the \(k\)-clique method. In this experiment, the maximum number of users in the community detected by the \(k\)-clique method is equal to 8 people. For the detail of the MAPE result in this proposed method is; when the amounts of users in the community are equal to 3 people, this means the result of the prediction or incorrect value or the MAPE result value will be 16.71%. When we checked the number of users in the community equals to 4 people, we found that the result value of the prediction or MAPE value is 15.21%. After that, when the amounts of users in the community are equal to 5 people, the result of the prediction or incorrect value or the MAPE result value becomes 15.15%. Later on, we found that the result value of the prediction or inaccurate value is equal to 13.87% when the number of a user is 6 users. However, if the number of users in the community equals 7 users, then the result value of the prediction or MAPE value is equal to 12.55%. Finally, when the maximum number of the user in the community was detected at the 8 people, we found that the value of prediction is equal to 10.27%. Figure 7 shows that the best outcome of the proposed method is 10.27%, while the value of k = 8. The MAPE's results of the collaborative filtering method, and maximal cliques method used to evaluate the preciseness of the proposed method. If the MAPE output value result becomes a low value, that means our proposed method is useful. First of all, the mean absolute percentage error for the proposed method will calculate. In brief, The result of the proposed method is displayed in various values, which depend on the value of k. The value of k is a number of users in each community. The result value of this proposed method is 10.27%, for k = 8 people. Once completion of the calculation of the result value of the proposed method. Comparatively, the result value of the collaborative filtering method is used to calculate, and the result was 18.88%. The workflow and calculates done for MAPE result of the collaborative filtering method was using the same way to the experimental of the proposed method.