文献类型： 外文期刊

作者： Ma, Wenming ¹ ; Li, Mingqi ¹ ; Dai, Jian ² ; Ding, Jianguo ³ ; Chu, Zihao ¹ ; Chen, Hao ¹ ;

作者机构： 1.Yantai Univ, Sch Comp & Control Engn, Yantai 264005, Peoples R China

2.Xinjiang Acad Agr Sci, Urumqi 830091, Peoples R China

3.Xinjiang Acad Agr Sci, Inst Agr Econ & Informat, Urumqi 830091, Peoples R China

关键词： food; heterogeneous graph; graph neural networks; recommendation systems

期刊名称：FOODS （ 影响因子：4.7； 五年影响因子：5.1 ）

ISSN：

年卷期： 2024 年 13 卷 13 期

页码：

收录情况： SCI

摘要： Food recommendation systems are becoming increasingly vital in modern society, given the fast-paced lifestyle and diverse dietary habits. Existing research and implemented solutions often rely on user preferences and past behaviors for recommendations, which poses significant issues. Firstly, this approach inadequately considers the nutritional content of foods, potentially leading to recommendations that are overly homogeneous and lacking in diversity. Secondly, it may result in repetitive suggestions of the same types of foods, thereby encouraging users to develop unhealthy dietary habits that could adversely affect their overall health. To address this issue, we introduce a novel nutrition-related knowledge graph (NRKG) method based on graph convolutional networks (GCNs). This method not only enhances users' ability to select appropriate foods but also encourages the development of healthy eating habits, thereby contributing to overall public health. The NRKG method comprises two key components: user nutrition-related food preferences and recipe nutrition components. The first component gathers nutritional information from recipes that users show interest in and synthesizes these data for user reference. The second component connects recipes with similar nutritional profiles, forming a complex heterogeneous graph structure. By learning from this graph, the NRKG method integrates user preferences with nutritional data, resulting in more accurate and personalized food recommendations. We evaluated the NRKG method against six baseline methods using real-world food datasets. In the 100% dataset, the five metrics exceeded the performance of the best baseline method by 2.8%, 5.9%, 1.5%, 9.7%, and 6.0%, respectively. The results indicate that our NRKG method significantly outperforms the baseline methods, including FeaStNet, DeepGCN, GraphSAGE, GAT, UniMP, and GATv2, demonstrating its superiority and effectiveness in promoting healthier and more diverse eating habits. Unlike these baseline methods, which primarily focus on hierarchical information propagation, our NRKG method offers a more comprehensive approach by integrating the nutritional information of recipes with user preferences.