通过图形对比学习来实现触发动作编程规则的推系统
Zhejun Kuang1,2,3, Xingbo Xiong1,2,3, Gang Wu4
1College of Computer Science and Technology, Changchun University, Changchun 130022, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
这项研究介绍了GCL4TAP,这是一个新的系统,用于为物联网 (IoT) 设备推触发动作编程 (TAP) 规则. GCL4TAP有效地建模用户与设备之间的关系和协作用户信息,以改善规则自动化.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 触发动作编程 (TAP) 通过用户定义的规则自动化物联网 (IoT) 设备.
- 越来越多的物联网设备使手动规则的创建变得复杂和耗时.
- 现有的TAP推系统忽视了用户规则协会和协作用户信息.
研究的目的:
- 为TAP规则提出GCL4TAP,这是一个基于学习的推系统,用于TAP规则.
- 通过结合跨用户规则关系和用户相似性来解决现有系统的局限性.
- 为了提高物联网设备自动规则发现的效率和准确性.
主要方法:
- 开发了DATA2DIV,这是一种数据分区方法,用于在用户规则双边图中表示跨用户规则关系.
- 构建了一个用户-用户图表,以根据所拥有的设备类别和数量捕捉用户相似之处.
- 利用图形对比学习来为用户和规则生成低维向量表示.
主要成果:
- 与最先进的方法相比,GCL4TAP在广泛的实验中表现出更高的性能.
- 该系统有效地模拟用户之间的协作信息,以改进规则建议.
- 在现实世界智能家居数据集上的实验结果验证了拟议的方法.
结论:
- GCL4TAP为触发行动编程的推系统提供了显著的进步.
- 图形对比学习方法有效地捕捉了物联网自动化场景中的复杂关系.
- 拟议的系统通过简化各种物联网设备的自动化来增强用户体验.
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