# Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts
## 7 Discussion  
[PERSON] et al. [14] and [PERSON] et al. [15] presented a developed system that could solve the cold-start problem by using demographic information. By using demographic information, we can visualize personalized information for any user without the need to have the user's history. Therefore, the proposed methodology improved the personalized visualization and can be combined with the existing content and collaborating filtering methods, which were utilized in some literature for developing personalized AR systems. Although the literature covers using demographic contexts for personalization, this research addressed combining the demographic and spatial-temporal contexts in order to develop the AR system. The AR system is utilized in real-time and in the real-world, so applying spatial-temporal contexts were necessary in order to develop the AR system. As a result, our methodology adapted the existing method for the AR system by combining the demographic and spatial-temporal contexts  
Comparison of the results of two machine learning methods, which include the DT and SVM methods, showed better performance with the SVM method by about 6% in regard to the overall accuracy. Furthermore, the high accuracy of the prediction of the user preferences, which were more that 70% for both methods, prove the relation between the user's interest in the touristic attractions and demographic contexts, which include age, gender, and education level. According to this result, better performance will be obtained if we collect more data for the training and testing methods. In addition, the user study results showed a high level of the user satisfaction, which is a more that 70% agreement. It indicates that the proposed methodology is toward developing ubiquitous GIS systems, because the ease-of-use and context-awareness are the two main features of the ubiquitous GIS systems. The demographic contexts made it possible to provide the right information to the user, and the spatial-temporal contexts enabled us to provide the right service at the right location and the right time. The AR technology made the use and learning of the tourism system more convenient by creating a natural and 3D user interface.  
Although we believe that this research provides value in the field of personalized AR tourism systems, it has some limitations. First, the user feedback was applied only for changing the place type, which the user is interested in, whereas it can be used in the learning process by using a reinforcement learning method in order to improve the prediction accuracy. Second, this research focused on the spatial ranking to visualize the touristic attractions through the personalized AR system according to the user's preferences and the spatial-temporal criteria. However, the AR system requires semantic ranking in addition to spatial ranking in order to rank the contents at each recommended tourism place. At each place, there are various contents that are valuable to provide to the tourists. For instance, the various contents of restaurants include menus, customer satisfaction, the restaurant capacity, and the crowdedness of the restaurant. These various contents should be ranked according to the user's preferences in the AR systems. Finally, this research can be improved by combining it with routing methods [30; 54] so that the user can be navigated to the ranked Pols.