一个新的团体旅游旅行推模型,用于个性化的旅游系统
1Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-kharj, Riyadh, Saudi Arabia.
PeerJ. Computer science
|February 3, 2025
概括
这项研究引入了一种使用群体优化 (ACO) 的新团体旅游推模型 (GTTRM). 它通过动态组建子组来增强个性化的旅行计划,以最大限度地提高团队满意度并最大限度地减少冲突.
科学领域:
- 人工智能的人工智能
- 推系统是一个推系统.
- 运营研究 运营研究
背景情况:
- 针对不同偏好的群体进行个性化旅行行程规划,会带来重大挑战.
- 现有的推系统往往难以在群体环境中平衡个人愿望,导致旅游体验不足最佳.
研究的目的:
- 提出一个新的团体旅游旅行推模型 (GTTRM),旨在优化团体满意度.
- 解决团队成员在旅行规划和执行过程中相互矛盾的偏好所带来的挑战.
主要方法:
- 使用殖民地优化 (ACO) 算法来找到最佳的旅行路线和活动时间表.
- 实施一个动态的子组组建机制,以适应在旅行期间不断变化的群组偏好.
- 开发一个推引擎,平衡个人满意度,尽量减少成员之间的冲突.
主要成果:
- 与基线模型相比,GTTRM显著提高了个体组成员的满意度水平.
- 在动态子集团管理中表现出卓越的表现,有效地处理多样化和冲突的偏好.
- 在生成个性化的团体旅行行程中实现了更高的优化效率.
结论:
- 拟议的GTTRM为个性化团体旅行建议提供了有效的解决方案.
- 动态子组组建是提高团体旅行满意度和适应能力的关键创新.
- 在团队旅游的优化和子组管理方面,GTTRM的表现优于现有的最先进模型.
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