A Contextual Multi-armed Bandit Approach to Personalized Trip Itinerary Planning

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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Publication Details

Published
2024-09-16
DOI
https://doi.org/10.1109/sm63044.2024.10733530
Citations
1
Primary Topic
Advanced Bandit Algorithms Research
Type
article
Field-Weighted Citation Impact
0.49
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A Contextual Multi-armed Bandit Approach to Personalized Trip Itinerary Planning

Alaa Khamis, Tianyu Shi, Mufeng Wang, Jiarui Zhang et al.
1 citations
Advanced Bandit Algorithms Research
0.49
article

A Contextual Multi-armed Bandit Approach to Personalized Trip Itinerary Planning

Alaa Khamis, Tianyu Shi, Mufeng Wang, Jiarui Zhang, Haowei Li
article en
1 citations

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.

Applied Science Private University (JO), University of Toronto (CA), General Motors (Canada) (CA)
Openalex Percentile: Top 30%
Advanced Bandit Algorithms Research
0.49
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