## 5 Experiment and Results
### Hotel Recommendation Service Test  
The system test at this level was performed by a default user in the Gwangjin-gue area of Seoul. In this test, the user was asked to operate the system at their location by performing the following steps: (1) entering user position by pressing the TRACKING button, (2) entering hotel items using the list box defined in the system, (3) storage of information from steps 1 and 2 as context by pressing the SET button, and (4) executing The system result by pressing the show map button. Figure 6 depicts the system procedure for this test. The results of the system test at this stage are shown in Figures 7 and 8. As shown in Figure 7, the user selected fast food, subways, and parks for the analysis. This information was stored in the system. Then, the system selected seven candidate hotels in the vicinity of the user using a 2000 m search radius. These candidate hotels were used by the TOPSIS model for the final hotel selection. The result of the system test at this stage is the selection of the hotel with the highest CI (in this case, 0.92) among all candidate hotels. The details of this comparison are presented in Table 3 and Figure 9.  
Figure 6: General procedure of the hotel recommendation service. Figure 8: Selected hotel visualization: (**a**) position visualization and (**b**) detail of the selected hotel. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Candidate Hotel** & **C1** & **C2** & **C3** & **C4** & **C5** & **CI** \\ \hline H Avenue Hotel & 4230.588 & 2221.525 & 1290.617 & 1 & 2 & 0.7501 \\ Konde Seongso & 3154.782 & 2445.536 & 1214.613 & 1 & 3 & 0.909 \\ Hotel The Designers & 4086.088 & 2201.338 & 1227.685 & 2 & 2 & 0.6207 \\ Premier Kondae & 3679.193 & 2257.693 & 1105.75 & 1 & 0 & 0.5249 \\ Plein Hotel & 4876.355 & 4247.521 & 1073.118 & 1 & 0 & 0.4429 \\ The Classic 500 & 5153.299 & 3011.659 & 1008.67 & 3 & 3 & 0.5121 \\ Hotel Cullinan2 & 3753.529 & 2284.855 & 1069.903 & 1 & 3 & 0.9278 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Candidate hotel criteria matrix: C1, C2, and C3 represent distances to the closest fast food, restaurant, subway, and park, respectively; C4 and C5 represent the cost and the number of hotel stars, respectively, while CI is the closeness index. Figure 7: User item setting: (**a**) user position tracking and (**b**) hotel item selection. Table 3 lists the following information concerning each candidate hotel: distances to the closest fast food restaurant, subway, and park as C1, C2, and C3, respectively; cost and number of hotel stars as C4 and C5, respectively, and the CI. Figure 9 depicts the CI for each candidate hotel (referred to as candidates in the figure). In the continuation of the system test in this step, the same step was repeated for all candidate hotels. Therefore, the system used clustering and TOPSIS models. Four clusters were created in this process, and the cluster agents were compared based on the five criteria C1, C2, C3, C4, and C5, as mentioned previously before using the TOPSIS method to select the best cluster. It must be noted that the five criteria for cluster agents are obtained by the average value of C1, C2, C3, C4, and C5 for each cluster's member, which are called C1 average, C2 average, C3 average, C4 average, and C5 average. Subsequently, the candidate hotels within the best cluster were compared using the TOPSIS method to select the final hotel. The comparison results are shown in Tables 4 and 5. Table 4 lists the average values of the C1, C2, C3, C4, and C5 criteria (defined in Table 3) of the candidate hotels within each cluster agent as C1 average, C2 average, C3 average, C4 average, and C5 average, respectively; it also lists the corresponding CIs. Cluster 2 has the highest CI. As a result, the selection of the final hotel will be done based on a comparison of candidate hotels belonging to cluster2. Table 5 lists the C1, C2, C3, C4, C5, and CI values for the candidate hotels within Cluster 2.