## 6 Discussion  
The experimental implementation of the system demonstrates the several innovations offered by the system at the application level, which distinguish it from existing systems [18; 30]. These innovations are as follows:  
* In the hotel recommendation service:
* The proposed system applies a combination of MCDM and context-awareness, which enables adaptiveness or dynamism of the models in the system. Based on this combination, the MCDM models configure their parameters based on context. As a result, in the MCDM model, the alternatives (i.e., candidate hotels) are generated based on the position of the user determined via the context using SF. Additionally, the MCDM model compares the generated candidate hotels based on predetermined criteria whose values are selected via user preferences. These criteria include the distances to the nearest transportation systems, historical attractions, and restaurants; notably, the types of transportation systems, historical attractions, and restaurants are selected by the user and stored in the context.
* Furthermore, the hotel selection model applies clustering to manage datasets involving a considerably large number of candidate hotels. This capability overcomes the limitation of MCDM-based models for handling many alternatives, such as candidate hotels for applications such as hotel selection. This also increases the performance of the system. In the AR visualization service:
* The AR model incorporates context-awareness to define the rules necessary for adjusting the properties of the information to be displayed concerning the visited object, such  
Figure 13: Usability testing results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Usability Item** & **Average of All User Scores** \\ \hline Efficiency & 4.159 \\ Effectiveness & 4.146 \\ User satisfaction & 4.028 \\ Overall average score & 4.112 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Usability testing results.  
as the text size and LOD. This analytical engine supports information from different context types, such as user and environmental contexts.
* This engine is a functional engine that offers two different modes, namely, automatic and manual. If the user selects the automatic mode, the information is configured based on the rules defined by the system; in the manual mode, the user enters their preferred text size and LOD.
* Furthermore, this engine employs spatial analysis methods, such as distance analysis. Such analysis is employed to adjust the LOD of information based on the distance between the user and the visited object. The ability of users closer to a point of interest (POI) to obtain more information is always a basic need that causes better management of the access level and reduces unnecessary information for the user and the system. Therefore, using the proposed engine and management of LOD based on distance leads to achievement of this aim.  
Despite the importance of UTS-mAR and the advantages offered by it, there are some limitations that can be improved upon in future attempts to develop the system. First, UTS-mAR is designed for utilization by a single user, implying that the system displays the output based on the interactions of a single user with the hotel selection and visualization services. Therefore, for subsequent development, the system can be designed such that it can accommodate multiple users and match output results across sets of similar users. Second, the design and implementation of the models in the information visualization section focus on outdoor locations; consequently, most context information is related to outdoor spaces. Therefore, there is scope to include more contexts or define more rules that can improve the quality of the system models for indoor spaces.