ExplainableTrip: An XAI-Driven Personalized Travel Recommendation System Using Gradient Boosting and SHAP
Bibliographic record
Abstract
Tourism is an essential sector of the world's economies, necessitating to have travel planning solutions that are personalised and efficient.This work therefore presents ExplainableTrip, an advanced travel recommendation system utilizing Explainable Artificial Intelligence (XAI) to provide expedient and interpretable trip suggestions.With different user preferences, travel constraints, and comprehensive point-of-interest data, generates highly accurate itineraries, achieving an overall accuracy of 98.49% and an F1score of 0.97.ExplainableTrip leverages SHAP values (SHapley Additive exPlanations), to explain how factors like travel distance, time constraints, user interests, and Point of Interest (POI) popularity influence final recommendations.This will enable users to understand why a given set of options is being suggested, thus trusting the system.The methodology used in the system is an elaborate study of POI and inputs from the users based on Jaipur, India, alongside advanced algorithms for distance computation, time slots allotment, and route optimization.SHAP analysis forms a core feature, which enables users to receive customized itineraries while understanding the rationale behind them.ExplainableTrip is an enhancement of travel planning with balance between AI-driven efficiency and interpretability, and combines the latest technology with user-centric transparency to meet contemporary travellers' need for personalization and clarity in trip planning.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".