## 3 Ubiquitous Tourist System Based on MCDM and AR
### UTS-mAR Methodology
#### 3.2.1 Hotel Recommender Model Based on SF and MCDM
This function creates a list of candidate hotels in the vicinity of the user based on the search radius. In this \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & \multicolumn{2}{c}{**Descriptions**} \\ \hline \multirow{4}{*}{User context} & User position & Estimated by the system via user tracking \\ \cline{2-3}  & & Selection of hotel items, such as: \\  & & Transportation type (bus, subway), \\  & User preference & Restaurant, fast food), \\  & & Attraction type (usual (park), museum, historical (castle)) \\  & & * These items are selected via the system interface \\ \hline \multirow{4}{*}{Environmental context} & \multirow{4}{*}{Non-spatial item} & Access to transportation, attractions, and restaurants \\  & & * Type of spatial items is entered by user. \\ \cline{1-1}  & & * Access to a spatial item is estimated by the system via spatial \\ \cline{1-1}  & & function (closest: minimum distance). \\ \cline{1-1} \cline{2-3}  & & Hotel cost and the number of stars (star rating) \\ \cline{1-1}  & & * Star number contains the number of stars in the star ratings \\ \cline{1-1}  & & of hotels. \\ \cline{1-1}  & & * Hotel cost is classified as follows: \\ \cline{1-1}  & & Class1: Cheap hotel: Defined with score equal to 1. \\ \cline{1-1}  & & Class2: Usual hotel: Defined with score equal to 2. \\ \cline{1-1}  & & Class3: Expensive hotel: Defined with score equal to 3. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hotel recommender model context. study, the search radius was set to 2000 m. SF estimates the minimum distance (closest distance) between the user and the candidate hotels using user and hotel locations, which are obtained from the user context and environment. The generated candidate hotels are provided as inputs to the MCDM method. * **MCDM for final selection among candidate hotels** In this step, the MCDM method applies the non-spatial and spatial criteria for candidate hotels. The spatial criteria include the calculation of the distance of the candidate hotels to the nearest transportation systems, historical attractions, and restaurants. This is achieved using the minimum distance function with the positions of candidate hotels and the facilities retrieved from the environmental context. The specific types of transportation, attractions, and restaurants are obtained from user context. While, the non-spatial criteria are retrieved from the environmental context. Spatial and non-spatial criteria create candidate hotel data, which are provided as inputs to the TOPSIS method with their weights. Different methods, such as the AHP [37], can be used to calculate the weights of these items. In this study, we consider 0.20 as the equal weight for all (five) items. Of course, our system is not sensitive to this value, and it can easily change for the optional user. To determine the final hotel, the TOPSIS [38] method calculates a closeness index (CI) for each candidate, and the hotel with the highest CI is selected. The reason for choosing the TOPSIS method in this section is its popularity and efficiency in similar research involving hotel selection based on the evaluation criteria. This method offers several advantages, as follows: The performance of this method is minimally dependent on the number of criteria. It can be used easily in positive and negative criteria. The application of the TOPSIS is practical for both quantitative and qualitative data. This method provides a quantitative index for ranking alternatives and choosing the better one. The simplicity of this method, as well as the possibility of a simple formulation offered by it, has made this a popular method for evaluating options [7; 18; 39]. * **MCDM and Clustering for final hotel selection from among candidate hotels** In addition to the method proposed in Appendix A (3.1), a modified method was developed for use in more complex situations, such as when the number of candidate hotels is large.