# A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism
## 6 Evaluation of System Presentation and Efficacy
### System Evaluation
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{1}{c}{**Standard**} & \multicolumn{1}{c}{**Items**} & \multicolumn{1}{c}{**The Proposed System**} \\ \hline \multirow{4}{*}{Usability} & \multirow{4}{*}{Quesenberry 5E} & Efficient & O \\  & & Effective & O \\  & & Engaging & O \\  & & Error Tolerant & X \\  & & Easy to Learn & O \\ \hline \multirow{8}{*}{Easy to use} & \multirow{8}{*}{Nielsen} & Visibility of system status & O \\  & & Match between system and the real world & O \\  & & User control and freedom & O \\  & & Consistency and standards & O \\  & & Error prevention & O \\  & & Recognition rather than recall & O \\  & & Flexibility and efficiency of use & O \\  & & Aesthetic and minimalist design & O \\  & & Help users recognize, diagnose, and recover from errors & X \\  & & Help and documentation & X \\ \hline \multicolumn{3}{c}{Total satisfaction} & \multicolumn{1}{c}{80\%} \\ \hline \end{tabular} Legend: “O” means to have this function; while “X” means none.  
\end{table} 
Table 3: Analysis of overall satisfaction with interface design of the system. (_BI_ should approach zero for the best balance). The verification results show that the human-machine interface of our proposed system can meet important design preferences and provide approximately optimal balance. The researchers also frequently use recall and precision, as used in information retrieval (IR), to measure the recommendation efficacy of the information recommended by the system. Recall means that clicked projects play a main role in the recommendation, and the equation for obtaining the proportion of the projects in the recommended projects is shown in Equation (8):  
\[recall=\frac{|\{recommended\_items\}\cap\{clicked\_items\}|}{|\{clicked\_items\}|} \tag{8}\]  
Precision means that recommended projects play a main role in the recommendation, and the equation for obtaining the proportion of the projects in the clicked projects is shown in Equation (9):  
\[precision=\frac{|\{recommended\_items\}\cap\{clicked\_items\}|}{|\{recommended\_items\}|} \tag{9}\]  
In view of the system's high reliability and validity, the researchers assume in the experiment that every recommending process contains at least one recommended item and gets at least one clicked item, in order to avoid the division of error by zero in the aforesaid two equations. The top-n recommendation set size is another factor affecting recommendation accuracy. The researchers also discuss a reasonable size for the recommendation set. To sufficiently provide a basis for good recommendation, which is large enough to provide comparisons with different parameters, i.e., recommender set size, the researchers performed preliminary experiments at St. John's University in Taiwan. The experiment's adopted top 3 to 5 recommendation, 10-20 recommender set sizes, and 20-40 candidate information set sizes; the total number of unit websites of the school is 62 and, at most, five in a building, with the exception of the administration building, which are used for measuring the accuracy of the recommendation. The test users include all of the freshmen students of this current year. The scenario is created to simulate students searching websites using a mobile device, with the system automatically providing related touring website information. The clicked items refer to when the student clicks on any recommended item and browses its corresponding website content. Note, we examined only the precision rate without calculating the recall rate; in a general recommendation system, we are mainly concerned with the correct recommendation. Figure 11 shows the results of the experiment. If the recommendation group changes, the average precision