空间布局优化模型集成分层注意力机制在智能旅游管理的开发中
Jie Ding1, Lingyan Weng1, Lili Fan1
1School of Tourism, Nanjing Institute of Tourism and Hospitality, Nanjing, Jiangsu, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究引入了一种新的多层注意力算法,用于准确的旅游需求预测和建议. 该模型通过预测旅行模式和优化目的地管理来增强旅游体验.
科学领域:
- 人工智能的人工智能
- 旅游管理 旅游管理
- 空间数据分析 空间数据分析
背景情况:
- 准确的旅游需求预测对于有效的目的地管理和企业战略至关重要.
- 当前的方法在分析游客行为和空间动态时,往往缺乏准确性.
- 提升旅游体验需要个性化的建议和优化的行程.
研究的目的:
- 为准确的旅游需求预测开发一个多层自我注意力机制推算法.
- 完善分析游客的情感倾向和空间偏好.
- 改善旅游部门的推系统和运营标准.
主要方法:
- 构建用于语义发现和位置巩固的多层注意力机制模型.
- 利用游客的偏好来预测类似的景点吸引力的可能性.
- 使用注意力机制与预测的乘客流网来推断多个地方的旅游需求.
主要成果:
- 拟议的模型在北京旅游数据中实现了8.11%的平均平均绝对百分比误差 (MAPE).
- 与替代深度学习模型相比,证明了更高的精度和有效性.
- 成功地推断了未来几个时期的多个风景区的旅游需求.
结论:
- 基于多层关注的空间布局优化方法学显著有利于旅游需求预测.
- 该算法增强了推系统,从而提高了运营标准和客户满意度.
- 这种方法为动态旅游管理提供了精确有效的解决方案.
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