Segmentation of Tourist Interest on Tourism Object Categories by Comparing PSO K-Means and DBSCAN Method
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".