A novel group tour trip recommender model for personalized travel systems
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new group tour trip recommender model (GTTRM) using ant colony optimization (ACO). It enhances personalized travel planning by dynamically forming subgroups to maximize group satisfaction and minimize conflicts.
Area Of Science
- Artificial Intelligence
- Recommender Systems
- Operations Research
Background
- Personalized travel itinerary planning for groups with diverse preferences presents significant challenges.
- Existing recommender systems often struggle to balance individual desires within a group context, leading to suboptimal travel experiences.
Purpose Of The Study
- To propose a novel Group Tour Trip Recommender Model (GTTRM) designed to optimize group satisfaction.
- To address the challenge of conflicting preferences among group members during travel planning and execution.
Main Methods
- Utilizing Ant Colony Optimization (ACO) algorithm to find optimal travel routes and activity schedules.
- Implementing a dynamic subgroup formation mechanism to adapt to evolving group preferences during the trip.
- Developing a recommendation engine that balances individual satisfaction and minimizes inter-member conflicts.
Main Results
- The GTTRM significantly improved individual group member satisfaction levels compared to baseline models.
- Demonstrated superior performance in dynamic subgroup management, effectively handling diverse and conflicting preferences.
- Achieved higher optimization efficiency in generating personalized group travel itineraries.
Conclusions
- The proposed GTTRM offers an effective solution for personalized group travel recommendations.
- Dynamic subgroup formation is a key innovation for improving group travel satisfaction and adaptability.
- GTTRM outperforms existing state-of-the-art models in both optimization and subgroup management for group tours.
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