Segmentation of Tourist Interest on Tourism Object Categories by Comparing PSO K-Means and DBSCAN Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Marketing in travel companies will usually offer promos or recommendations regarding various categories of random tourist objects to their customers.The promo or recommendation contains categories of tourist objects that are frequently visited and had good ratings from many customers.However, because companies do not really know and understand the characteristics or interests of each customer, sometimes some promos do not match their interests so that they are not interested in taking the promos that are offered.There are already several papers that discuss tourism recommendations, but they only focus on 1 tourist spot or tourism object category.Based on these problems, this thesis is made to discuss the segmentation of tourist interest in tourism object categories by comparing the PSO K-Means method and the DBSCAN method, which is about recommendations for more specific tour packages according to rating.Characteristics or similar interests between 1 tourist and other tourists will be grouped into 1 cluster.From each cluster that is formed, it can make it easier for companies to know what categories of tourist objects each customer is interested in or like and be able to offer promos or recommendations for tour packages according to tourist interests.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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