A Contextual Multi-armed Bandit Approach to Personalized Trip Itinerary Planning
Bibliographic record
Abstract
With the rise in people’s mobility and the flourishing of global tourism in recent years, there has been a notable interest in personalized trip planning. Trip itinerary planning (TIP) refers to the process of organizing and scheduling various elements of a journey, such as transportation, accommodations and activities, into a coherent and efficient plan. This paper particularly focuses on route personalization through points of interests (POIs), taking into account aspects such as budget constraints, hotel selection, users’ preferred POI categories, the duration of the trip, and the overall route length. To address these considerations, we implement Contextual Multi-armed Bandits (CMAB), a robust methodology where the decision-making is influenced by additional contextual information such as constraints and requirements of each traveller. The effectiveness of the proposed approach is validated by comparing against a baseline model in terms of user satisfaction and the time required to generate results. This paper demonstrates the potential of CMAB in personalized itinerary 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".