PredicTour: Predicting Mobility Patterns of Tourists Based on Social Media User’s Profiles
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes PredicTour, an approach to process check-ins made by users of location-based social networks (LBSNs), and predict mobility patterns of tourists visiting new countries with or without previous visiting records. PredicTour is composed of three key parts: mobility modeling, profile extraction, and tourist mobility prediction. In the first part, sequences of check-ins within a time interval are associated with other user information to produce a new structure called “mobility descriptor”. In the profile extraction, self-organizing maps and fuzzy C-means work jointly to group users according to their mobility descriptors. PredicTour then identifies tourist profiles and estimates mobility patterns of tourists visiting new countries. When comparing the performance of PredicTour with three well-known machine learning-based models, the results indicate that PredicTour outperforms the baseline approaches. Therefore, it is a good alternative for predicting and understanding international tourists’ mobility, which has an economic impact on the tourism industry when services and logistics across international borders should be provided. The proposed approach can be used in different applications, such as in recommender systems for tourists or in decision-making support for urban planners interested in improving tourists’ experiences and attractiveness of venues through personalized services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it