## 7 Conclusions  
This study presents a ubiquitous system for tourism that uses a combination of MCDM, AR, and context-awareness, called UTS-mAR, which can be adapted based on user and environmental contexts. The hotel recommendation component of this system generates several candidate hotels based on the user's location, compares these candidates via MCDM using the TOPSIS method based on several criteria, and selects a final hotel. Subsequently, the selected hotel is displayed on Google Maps to facilitate navigation to the hotel for users. Additionally, an extended version of the hotel recommendation model, employing a combination of clustering and the TOPSIS method, was developed to manage the analysis of large datasets, such as those that include all candidate hotels. In the AR visualization component, UTS-mAR applies contextual information such as tourist age, presence of vision problems, and time of day during system operation to set the text size of outputted information. Next, this component uses the distance between the user and the visited object to adjust the LOD of information using the models available in the system. Finally, the AR model uses this text size and LOD of the information to augment information concerning the visited object. Additionally, the proposed system was evaluated via a usability test in terms of system design and user acceptance of output based on a five-point Likert scale. This evaluation yielded an overall average user score of 4.112, which indicates the acceptance of the system by users, owing to its proximity to the highest level of the Likert scale. However, the proposed system offers scope for future development. The criteria for hotel selection could be expanded to accommodate criteria based on temporal events, such as the distance of the hotels from COVID-19 hotspots, or the air pollution quality of the region where the hotels are located. Furthermore, the user's view of assessment-related hotels could be entered as criteria to the system. Moreover, criteria could be generated using smart methods; for example, machine learning could be used to select the attraction type for a user based on the user's characteristics and the type of tourists. In the visualization service, the accuracy of the tracking and object recognition methods could be increased by using novel tracking methods and deep learning models, respectively. Additionally, the visualization model based on AR technology could be completed by adding three-dimensional data, such as the three-dimensional model during application of the LOD setting. Finally, this model could be improved by adding more parameters for better flexibility of the model, such as adding screen size in the text-size-setting model. The results of the visualization model will be assessed using performance analysis during the evaluation analysis.  
Conceptualization, S.R., A.S.-N. and H.-S.K.; Data curation, S.R. and J.-W.L.; Formal analysis, S.R. and H.-S.K.; Funding acquisition, S.-M.C.; Investigation, A.S.-N.; Methodology, S.R., A.S.-N. and S.-M.C.; Project administration, S.-M.C.; Resources, S.R.; Supervision, A.S.-N. and S.-M.C.; Validation, J.-W.L. and S.-M.C.; Visualization, S.R., A.S.-N., H.-S.K. and J.-W.L.; Writing--original draft, S.R.; Writing--review & editing, S.R., A.S.-N. and S.-M.C. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program (project no. P0016038).  
The authors declare no conflict of interest.