## Appendix A Algorithm Pseudocode  
```
1. Preparing context
2. Preparing environmental context
3. Spatial data include locations of hotels, transportation, restaurants, and features of tourist attractions, while non-spatial data include hotel features, namely, their cost and star ratings. 4. Preparing user context
5. Prepare user position using fused tracking and transformation, and accept user preferences, including selection of transportation type, restaurant type, and attraction type through the system interface. 6. SF method for candidate hotel generation
7. Use user position and hotel feature position from the user and environmental contexts. 8. For each hotel:
9. Estimate distance between the user and hotel based on user and hotel locations. 10. If distance (hotel, user) \(\leq\) search radius: 2000 m.
11. Save hotel location in the list as a candidate hotel location. 12. Hotel selection model
13. Run Model 3.1 for final hotel selection from normal data and Model 3.2 for final hotel selection from large data, where large data means the number of candidate hotels exceeds the threshold of 30. ### MCDM for selection of final hotel from among candidate hotels

Preparing candidate hotel data

Use transportation, attraction, and restaurant feature locations, and candidate hotel locations obtained from the environmental context. For each candidate hotel:

Estimate distance to the closest transportation, attraction, and restaurant features. Use candidate hotel cost and star rating from context. Save the above distance, cost, and star rating as candidate hotel data. Run TOPSIS using candidate hotel data. Estimate closeness coefficients for each candidate hotel (CI) using the formula below:

\[\text{CI}_{\text{i}}=\frac{\text{d}_{i}^{-}}{\text{d}_{i}^{+}+\text{d}_{i}^{- }},\]

\[\text{where }:\]

\[\text{d}_{i}^{+}\]

is the distance from the positive ideal solution;

\[\text{d}_{i}^{-}\]

is the distance from the negative ideal solution;
```
Obtain the location of the hotel with the highest CI from environmental context. Note: see [38] for details related to the TOPSIS formula. ```
**Algorithm A1**_Cont_. ### MCDM and clustering for final hotel selection from among candidate hotels

**Preparing candidate hotel data**

Use transportation, attraction, and restaurant feature locations, and candidate hotel location obtained from environmental context. For each candidate hotel:

Estimate distance to the closest transportation, attraction, and restaurant features. Use candidate hotel costs and number of hotel stars from context. Save the above distance, cost, and number of hotel stars as candidate hotel data. Run normalization on candidate hotel data. For criteria values belonging to jth column (\(\rm C_{i}^{N}\)), perform the following normalization:

\(\rm C_{i}^{N}=\frac{C_{i}-min(C_{i})}{Max(C_{i})-Min(C_{i})}\),

where :

\(\rm i=1:\ldots,n;i=row\) number; \(n=number\) of hotels. Run hierarchical clustering using normalized candidate hotel data. Identify cluster numbers for candidate hotels. Generate cluster agents using the average of cluster-member criteria values. Use cluster agent data in TOPSIS to find the best cluster. Compare candidate hotels within the best cluster using the TOPSIS method. Select the hotel with the highest CI. Obtain the location of the selected hotel from environmental context. Note: see [19; 40] for more details related to hierarchical clustering. **Visualization**

Use the location of the selected hotel. Transform this hotel location into geographic coordinates (longitude and latitude). Visualize the geographic coordinates in Google Maps. ```
**Algorithm A2**AR visualization model based on augmented reality. ```
1. Preparing context
2. Preparing user context
3. Obtain user position using fused tracking, user age, and presence of vision problems entered via the system interface. 4. Prepare the environmental context
5. Enter time of day through the system interface, and store visited object data comprising the location and information in the system. **2. Text-size-setting model**
6.