GeM:高斯嵌入式与多跳转图形转移为下一个POI建议
Wenqian Mu1, Jiyuan Liu2, Yongshun Gong1
1School of Software, Shandong University, Jinan, China.
概括
GeM通过模拟多跳用户移动模式来增强下一个感兴趣点 (POI) 建议. 这种统一的框架通过考虑用户偏好和客观因素来改善基于位置的服务来提高准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 下一个感兴趣点 (POI) 建议对于基于位置的服务至关重要,它依赖于历史用户轨迹分析.
- 当前的方法经常使用图形结构和注意力机制,但忽视了用户移动模式中的多跳转依赖性.
研究的目的:
- 提出GeM,一个新的框架,准确的下一个POI建议.
- 通过结合多跳图关系和用户偏好来解决现有方法的局限性.
主要方法:
- GeM利用高斯分布和Mahalanobis距离进行主观用户偏好建模.
- 一个目标模块挖掘全球轨迹图信息和多跳的依赖关系.
- 矩阵分解被用来学习用户-POI交互.
主要成果:
- 拟议的GeM框架有效地捕捉了复杂的用户移动模式.
- 在真实世界数据集上的实验表明,与最先进的方法相比,性能优越.
- GeM实现了更准确的用户行为模式表示.
结论:
- 通过整合主观和客观因素,GeM为下一个POI建议提供了统一的方法.
- 该框架模拟多跳转依赖性的能力提高了预测准确度.
- GeM代表了基于位置的推系统的重大进步.
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