RARE:正确任务的正确算法;基于机器学习的多模型方法,用于旅游路线和景点推
1Henan Vocational College of Information and Statistics, Zhengzhou, Henan, China.
PeerJ. Computer science
|June 26, 2025
概括
这项研究引入了一种新的机器学习系统,用于个性化旅行建议. 它动态优化旅游路线,提高旅游规划和用户体验在不断增长的旅游业.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 旅游信息学 旅游信息学
背景情况:
- 旅游业是主要的经济驱动力,增加了对高效旅行计划的需求.
- 个性化推的需求很大,但传统方法是不够的.
- 需要动态,人工智能驱动的解决方案来适应不断变化的旅游景观.
研究的目的:
- 开发一个新的旅游推系统,使用多个机器学习算法.
- 以动态的方式提供个性化的旅游景点和路线建议.
- 提高旅行计划的效率和用户体验.
主要方法:
- 一个2D网格模型代表了旅游地图与相互连接的节点.
- 长期短期记忆 (LSTM) 预测了现场相关性.
- 支持矢量机 (SVM) 将现场名称分类.
- 深度首次搜索 (DFS) 产生最佳路线.
- K-means集群分配了区域管理的集群领导者 (CL).
主要成果:
- 该系统基于简单的文本查询提供了优化的旅行路线.
- 在增强旅行数据集上的实验评估证明了有效性.
- 该模型成功地提高了旅游规划和用户满意度.
结论:
- 拟议的机器学习框架为旅游推提供了一个动态和适应性的解决方案.
- 这种方法有可能显著推进智能旅游系统.
- 个性化,优化的路线改善了整体旅游体验.
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