Modelling the Accommodation Preferences of Tourists by Combining Fuzzy-AHP and GIS Methods
Bibliographic record
Abstract
Finding the place of accommodation is one of the most crucial issues during a journey. This study aims to support the decision-making of tourists for choosing the optimal accommodation by combining fuzzy analytic hierarchy process (FAHP) and geographic information system (GIS) techniques. The adopted criteria are the cost per room, the distance from the center, the level of security, the place rating, and the availability of free cancellation and breakfast. Due to some uncertainty and diversity of criteria, the FAHP approach is applied to consolidate tourists’ decisions by applying criteria weighting, while the GIS is used to overlay the weighted criteria and to visualize the ranked places of accommodation on a map. The combined technique is applied on a case study in Budapest City, where the analysis is conducted on 364 places of accommodation. The results show that half of the places are recommended for tourists, and more than fifth of the accommodations are highly recommended. Furthermore, it can be concluded that the cost per room was the highest influential criterion with 0.233 importance weight, followed by the security level with 0.205. The lowest factor affecting the choice of accommodation was the free cancellation service. It was demonstrated that the rating weight importance was 0.182, while the breakfast and the distance from the center had approximately the same importance. As a recommendation, some improvements on the accommodation, such as decreasing the cost per room, enhancing the services, or developing the quality of the places, would increase their attractiveness for tourists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".