## 3 Ubiquitous Tourist System Based on MCDM and AR
### UTS-mAR Methodology
#### 3.2.1 Hotel Recommender Model Based on SF and MCDM  
In our system, the hotel recommender model is based on SF and MCDM. This model uses context information and performs SF to generate a list of candidate hotels in the vicinity of the user. Subsequently, the model compares the generated candidate hotels using the MCDM method with TOPSIS. This comparison is based on various criteria, such as cost, number of hotel stars, and distances from candidate hotels to transportation, attractions, and restaurants. The preferred types of transportation, attractions, and restaurants are previously selected by the user and stored in the context. The result of this process is the selection of the final hotel, which is displayed via Google Maps to facilitate easy navigation for the user. An extended version of the proposed model was considered for cases when the number of candidate hotels was too large to be processed via the MCDM method. To solve this problem, the extended model applies both clustering and MCDM after performing SF. The hotel recommender model is shown in Figure 3. This model involves various steps, as follows: (1) Context preparation, which provides data for the system model. This data model is related to the environment and users. (2) Application of SF for candidate hotel generation. In this step, a list of candidate hotels in the vicinity of the user is created. (3) Use of the hotel selection model to select the final hotel from the list of candidates. This step uses one of the following models:  
Figure 3: Hotel recommender model. (3-1) MCDM for final hotel selection in the case of a small number of candidate hotels. The candidate hotels are compared via the MCDM method based on TOPSIS, and the final hotel is selected based on the TOPSIS index. (3-2) MCDM and clustering for final hotel selection in the case of large numbers of candidate hotels. Here, the candidate hotels are analyzed based on a combination of clustering and MCDM to determine the final hotel to be selected. (4) Visualization model. This step displays the output of the selected model to the user. Here, the location of the selected hotel is displayed on the Google Maps interface to enable easy navigation for the user. These four steps are described in detail below. * **Preparing context** As shown in Algorithm A1 (Appendix A), context preparation is the first step in the hotel recommender model. Table 1 lists the contexts necessary for achieving this aim. The context is obtained from different sources. The environmental context is obtained from geospatial data. These data are stored in the SpatiALite database in the user's mobile phone, and include two types of information: spatial and non-spatial. Spatial information includes the location of the hotel and the hotel items, such as attractions, restaurants, and transportation. Non-spatial information also includes information related to hotels, such as the number of hotel stars and cost. The user context includes the user location and preferences. The user location was determined via fused tracking, which estimates the user position by combining information from different sources, such as Wi-Fi, telecommunication towers, and global positioning systems (GPSs). The user location is provided as an input to the spatial transformation method, which returns an estimation of the user position in metric units using the universal transverse Mercator projection system. This position is stored in the system. Defining user preferences involves the selection of hotel attributes through the system interface. The user selects the type of transportation system (e.g., bus, subway), type of restaurant (e.g., restaurant, fast food), and type of historical attraction (usual (here, parks), museum, or historical (here, castles)). Section 4.1 provides more details concerning the steps for context preparation. In the Table 1, some explanation detail related table items are shown with \"\". * **SF method for candidate hotel generation** Running SF is the next step in the hotel recommender model.