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基于时间多层序列神经网络的个性化旅游推模型.

XueFei Xiao1, ChunHua Li2, XingJie Wang1

  • 1School of Computer Science and Technology, Yibin University, Yibin, 644000, China.

Scientific reports
|January 2, 2025
PubMed
概括

本研究介绍了临时多层序列神经网络 (TMS-Net),用于个性化的旅游路线建议. TMS-Net有效地处理复杂的轨迹数据,提高准确性和相关性,以增强旅行者体验.

关键词:
深度学习是一种深度学习.神经网络的神经网络的神经网络个性化的路线推路线建议.自我注意力机制机制

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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:

  • 个性化旅游路线推面临挑战,因为大型的时空空间数据跨度.
  • 现有的系统很难有效地整合历史数据,用户偏好和实时条件.
  • 高相关性和准确性对于先进的个性化旅行系统至关重要.

研究的目的:

  • 提出一个新的个性化旅游路线推模型,即时间多层序列神经网络 (TMS-Net).
  • 为了应对旅游轨迹数据中长时间和空间跨度所带来的挑战.
  • 提高个性化路线建议的准确性和相关性.

主要方法:

  • 开发了具有适应性轨迹细分的TMS-Net,以管理时空数据复杂性.
  • 集成了一个自我注意机制与相对位置信息来捕捉路径关系.
  • 利用多层长短期记忆网络进行深度时间依赖模型的旅行路线.
  • 在成都市的600多万个轨迹数据点 (2016-2022) 上训练了模型.

主要成果:

  • 确定最佳轨迹细分间隔为0.8至1.2小时.
  • 获得了88.6%的推准确率.
  • 记录了1.23的Haversine距离误差,表明精确的兴趣点识别.
  • 证明有效地识别长期旅行行为.

结论:

  • 通过准确识别感兴趣点,TMS-Net显著改善了个性化的旅游路线建议.
  • 该模型为个性化旅行推系统提供了新的方法见解.
  • 适应细分和深度时间建模是提高建议相关性和准确性的关键.