健康意识的食品推系统,在异质图表中双重关注
Saman Forouzandeh1, Mehrdad Rostami2, Kamal Berahmand3
1School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia.
Computers in biology and medicine
|December 28, 2023
概括
这项研究引入了一种健康意识的食品推系统,在异质图中双重关注 (HFRS-DA),以发现健康的食谱. 新的框架有效地从复杂的数据中学习,促进更健康的饮食习惯.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 推系统 (RS) 在整合食品和健康建议的各种数据方面面临挑战.
- 在异构的图形结构数据中发现有意义的关系对于有效的建议至关重要.
研究的目的:
- 提出一个新的框架,健康意识的食品推系统与双重注意力在异质图 (HFRS-DA),无监督的代表学习.
- 应对纳入异质信息和发现食品和健康背景中的实体关系的挑战.
主要方法:
- 使用注意力技术对节点特征和边缘重建.
- 采用双层次的注意力机制,以增强对属性图表表示的无监督学习.
- 在异质图中分析配方组件及其邻居.
主要成果:
- 在Allrecipes数据集上,HFRS-DA的表现优于现有的方法.
- 该框架有效地利用异质信息并发现语义关系.
- 证明了赋值图表表示的增强无监督学习,对于数据稀缺场景至关重要.
结论:
- 通过发现流行和健康的食谱,HFRS-DA有效地促进健康的饮食习惯.
- 该框架可实现有效的节点嵌入生成未见数据,支持感应和传导学习.
- 强调高级图表表示学习对于个性化的健康和营养建议的重要性.
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