Food classification is an important part of food safety standards. In this paper, we propose a novel visual comparative analysis method for food classification trees (FCTs) in pesticide maximum residue limit (MRL) standards, called TreeMerge, to lay the foundation for a comprehensive comparison of pesticide MRL standards. First, a union tree is constructed by combining the two FCTs to be compared. Then, sunburst with an embedded chordal graph (SECG) and overlapping circular treemap (OCT), which are two new visualization solutions designed in this paper, are used to show the similarities and differences in a union tree. SECG can express the hierarchical structure and the similarity between corresponding nodes in the union tree at the same time. OCT uses an improved nested Venn diagram (overlapping circle) to express the attribute values in each layer of the union tree and uses a circle-filling layout algorithm based on the testing circle to improve the readability and space utilization of the view. Finally, a visual analysis system for comparing FCT, named FCTvis, is designed and implemented to support the exploration of the structural difference pattern of food classification in the two MRL standards and the quantity or scale of residue limits in various foods. The effectiveness of TreeMerge was verified by case studies on pesticide MRL standards in the Chinese Mainland and Chinese Hong Kong.
Current food recommender systems tend to prioritize either the user’s dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account the user’s personalized health requirements, in addition to their dietary preferences. Our work comprises three perspectives. Firstly, we propose a collaborative recipe knowledge graph (CRKG) with millions of triplets, containing user–recipe interactions, recipe–ingredient associations, and other food-related information. Secondly, we define a score-based method for evaluating the healthiness match between recipes and user preferences. Based on these two prior perspectives, we develop a novel health-aware food recommendation model (FKGM) using knowledge graph embedding and multi-task learning. FKGM employs a knowledge-aware attention graph convolutional neural network to capture the semantic associations between users and recipes on the collaborative knowledge graph and learns the user’s requirements in both preference and health by fusing the losses of these two learning tasks. We conducted experiments to demonstrate that FKGM outperformed four competing baseline models in integrating users’ dietary preferences and personalized health requirements in food recommendations and performed best on the health task.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.