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冯丹, 月小琪, 秦红星, 胡海波. 中华菜系饮食数据可视分析[J]. 计算机辅助设计与图形学学报, 2023, 35(4): 562-574. DOI: 10.3724/SP.J.1089.2023.19409
引用本文: 冯丹, 月小琪, 秦红星, 胡海波. 中华菜系饮食数据可视分析[J]. 计算机辅助设计与图形学学报, 2023, 35(4): 562-574. DOI: 10.3724/SP.J.1089.2023.19409
Feng Dan, Yue Xiaoqi, Qin Hongxing, and Hu Haibo. DietVis: Visual Analytics System for Chinese Diet Data Based on Cuisines[J]. Journal of Computer-Aided Design & Computer Graphics, 2023, 35(4): 562-574. DOI: 10.3724/SP.J.1089.2023.19409
Citation: Feng Dan, Yue Xiaoqi, Qin Hongxing, and Hu Haibo. DietVis: Visual Analytics System for Chinese Diet Data Based on Cuisines[J]. Journal of Computer-Aided Design & Computer Graphics, 2023, 35(4): 562-574. DOI: 10.3724/SP.J.1089.2023.19409

中华菜系饮食数据可视分析

DietVis: Visual Analytics System for Chinese Diet Data Based on Cuisines

  • 摘要: 饮食文化是中华文明的重要组成部分,对饮食文化中八大菜系的探究有助于传承和弘扬中华文明.为了向人们进行合理的食谱推荐,提出一个可视分析系统DietVis.首先,设计一种用于表达菜系多维属性的新颖视图,帮助用户分析菜系构成特征;其次,通过对食谱的食材构成和烹饪手法等进行聚类分析,让用户可以多角度地探究食谱间的关联;并结合食谱搭配的关联规则进行推荐,以满足不同用户的检索需求;最后,通过多视图联动构建出完整的饮食数据可视分析系统,支持用户在多层面对菜系及食谱进行探索和对比分析.通过案例分析和用户评估实验,采用李克特量表对调查问卷进行分析,验证了可视化视图的有效性以及系统的实用性.

     

    Abstract: Food culture is an important part of Chinese civilization. The exploration of eight cuisines in food culture is conducive to the inheritance and promotion of Chinese civilization. A visual analysis system DietVis is proposed in order to make reasonable diet recommendations to people. Firstly, a novel view is designed to express the multidimensional attributes of cuisines to help users analyze the constituent features of cuisines. Secondly, through clustering analysis of ingredients and cooking techniques of recipes, users can explore the association between recipes from multiple perspectives. Thirdly, combine the association rules of recipe collocation to recommend to meet the retrieval needs of different users. Finally, a complete visual analysis system of dietary data is constructed through multi-view linkage, which supports users to explore and compare cuisines and recipes in multiple layers. Through case analysis and user evaluation experiment, and using Likert scale to analyze the questionnaire, the effectiveness of visual view and the practicability of the system are verified.

     

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