基于知识图和多任务学习的健康意识的食物建议
Yi Chen1, Yandi Guo1, Qiuxu Fan1
1Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
Foods (Basel, Switzerland)
|May 27, 2023
概括
这项研究引入了一种新的健康意识食品推模型 (FKGM),该模型既考虑饮食偏好,也考虑个性化健康需求. 通过将用户数据集成到一个协作式食谱知识图中,FKGM提高了个性化的食物建议.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 营养科学 营养科学
背景情况:
- 现有的食品推系统往往无法平衡饮食偏好与个性化的健康要求.
- 需要先进的模型,可以将复杂的用户健康数据集成到食品建议中.
研究的目的:
- 开发一种新的健康意识的食品推模型 (FKGM),该模型结合了个性化的健康要求和饮食偏好.
- 创建一个全面的协作食谱知识图 (CRKG) 以更丰富的食品数据表示.
主要方法:
- 构建一个配方知识协作图 (CRKG) 图表,其中包含数以百万计的用户配方交互和成分关联.
- 开发一个基于分数的方法来评估食谱的健康性与用户偏好.
- 实施FKGM,一个知识图嵌入和多任务学习模型,利用一个知识意识的注意力图卷积神经网络.
主要成果:
- 与四个基线模型相比,FKGM在整合饮食偏好和个性化健康要求方面表现优越.
- 该模型在与健康相关的推任务上取得了最佳表现.
- 在CRKG中,FKGM有效地捕捉了用户和食谱之间的语义关联.
结论:
- 拟议的FKGM模型在个性化健康食品建议方面取得了重大进展.
- 将个性化的健康要求与饮食偏好相结合,导致更有效的食物建议系统.
- 知识图嵌入和多任务学习是建立健康意识推系统的强大技术.
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