# Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts
###### Abstract  
A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users' demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%. augmented reality; big data; tourism; decision tree; VIKOR; SWARA +
Footnote †: journal: Medical  
field [4; 5], data modeling [2], and big data [6; 7] integration, and personalizing the information visualization according to the user preferences [8; 9]. With the personalization process of developed AR systems, the user preferences were extracted by either the user inputs or by mining the user history. Personalization by the user inputs [8; 9] and history have faced the problem for the new users, because there is no history, so it is difficult to insert preference for the users. There is no research that designed a personalized AR system that uses demographic information. On the other hand, the use of demographic contexts has been confirmed in order to improve the personalization process [10; 11; 12; 13], especially for solving cold-start problems [14]. In addition, it was also evaluated in the tourism field by different studies [10; 15]. However, they have not enabled the users to make decisions at any location and time by considering the spatial parameters and the decision-making methods. The purpose of this research is to develop a personalized AR tourism system that is based on both the user demographics and the spatial-temporal contexts by using and analyzing big data produced by the users. In order to design this system, the machine learning and the decision-making methods are applied in order to predict the type of attractive place where the user is interested and to rank the attractive places. In the step of predicting the place type, the machine learning method is trained according to the demographic contexts, which include the user age, gender, and the education level. After the place is predicted, the places are ranked by considering time, distance, popularity, and the neighborhood of the tourist places. Finally, the system is evaluated in terms of the accuracy of the machine learning method by comparing the accuracy of the Decision Tree (DT) and the Support Vector Machine (SVM) methods, accuracy of the two algorithms, and the usability of the personalized AR based tourism system. Summarizing, the main novel contributions of the research follow:  
* We develop a methodology to combine the demographic contexts and the spatial-temporal criteria using machine learning and decision-making methods in order to visualize the personalized information to the user through the AR technology. * We develop a personalized AR tourism system that is based on the demographic and spatial-temporal contexts and to evaluate it through a user study. The structure of this article follows.