A Contextual Multi-armed Bandit Approach to Personalized Trip Itinerary Planning
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
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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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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