对于下一个POI推的多颗粒度对比学习模型
Yunfeng Zhu1, Shuchun Yao1, Xun Sun2
1Suzhou Industrial Park Institute of Service Outsourcing, Suzhou, China.
Frontiers in neurorobotics
|July 1, 2024
概括
本研究引入了多细分化的对比学习 (MGCL),通过整合位置,区域和类别数据来增强下一个感兴趣点 (POI) 建议. MGCL有效地解决了数据稀疏性,并改善了用户偏好学习,以便更准确地预测POI.
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) 建议根据用户的历史活动预测用户的下一个目的地.
- 现有的方法在数据稀疏性方面扎,只使用位置级别的检查轨迹.
- 利用区域层面和类别层面的POI序列可以减轻稀疏性并改善建议.
研究的目的:
- 为下一个POI建议提出一个新的多细分化对比学习 (MGCL) 框架.
- 有效地利用POI序列的不同细分度的协作信息.
- 通过解决数据稀疏性,增强用户偏好学习和推性能.
主要方法:
- 构建位置级别的POI图表,类别级别和区域级别的序列.
- 采用图形卷积网络 (GCNs) 来实现跨用户的序列模式和个人用户模式的自我注意网络.
- 应用对比学习来捕获多细分序列之间的协作信号.
- 联合培训推和对比学习任务.
主要成果:
- 拟议的MGCL方法与现有最先进的方法相比,显示出更高的性能.
- 多颗粒度表示有效地增强了用户偏好学习.
- 对比式学习成功地捕获了不同POI序列细分度的协作信号.
结论:
- 通过整合不同的数据细节,MGCL在下一个POI建议中提供了显著的进步.
- 该框架有效地克服了传统方法中数据稀疏性的局限性.
- 未来的工作可以探索进一步改进捕获复杂的用户行为和上下文信息.
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