In recent years, the use of data-driven methods has provided insights into
underlying patterns and principles behind culinary recipes. In this exploratory
work, we introduce the use of topological data analysis, especially persistent
homology, in order to study the space of culinary recipes. In particular,
persistent homology analysis provides a set of recipes surrounding the
multiscale "holes" in the space of existing recipes. We then propose a method
to generate novel ingredient combinations using combinatorial optimization on
this topological information. We made biscuits using the novel ingredient
combinations, which were confirmed to be acceptable enough by a sensory
evaluation study. Our findings indicate that topological data analysis has the
potential for providing new tools and insights in the study of culinary
recipes.

利用拓扑数据分析，尤其是持久同调，研究了烹饪食谱的空间，通过在这些拓扑信息上进行组合优化，提出了生成新的食材组合的方法，并通过感官评估研究证实了这些新的食材组合的可接受性，研究结果表明拓扑数据分析在研究烹饪食谱中具有提供新工具和见解的潜力。

菜谱空间的拓扑分析

A topological analysis of the space of recipes

Cooking recipes allow individuals to exchange culinary ideas and provide food
preparation instructions. Due to a lack of adequate labeled data, categorizing
raw recipes found online to the appropriate food genres is a challenging task
in this domain. Utilizing the knowledge of domain experts to categorize recipes
could be a solution. In this study, we present a novel dataset of two million
culinary recipes labeled in respective categories leveraging the knowledge of
food experts and an active learning technique. To construct the dataset, we
collect the recipes from the RecipeNLG dataset. Then, we employ three human
experts whose trustworthiness score is higher than 86.667% to categorize 300K
recipe by their Named Entity Recognition (NER) and assign it to one of the nine
categories: bakery, drinks, non-veg, vegetables, fast food, cereals, meals,
sides and fusion. Finally, we categorize the remaining 1900K recipes using
Active Learning method with a blend of Query-by-Committee and Human In The Loop
(HITL) approaches. There are more than two million recipes in our dataset, each
of which is categorized and has a confidence score linked with it. For the 9
genres, the Fleiss Kappa score of this massive dataset is roughly 0.56026. We
believe that the research community can use this dataset to perform various
machine learning tasks such as recipe genre classification, recipe generation
of a specific genre, new recipe creation, etc. The dataset can also be used to
train and evaluate the performance of various NLP tasks such as named entity
recognition, part-of-speech tagging, semantic role labeling, and so on. The
dataset will be available upon publication: this https URL.

此研究介绍了一种基于领域专家知识和主动学习技术的新数据集，其包含了 200 万种用各种类别标记的烹饪食谱，包括糕点、饮料、非素食、蔬菜、快餐、谷物、餐点、配菜和融合九个类别，并且可以用于机器学习任务和各种自然语言处理任务的训练和评估。

基于主动学习的三百万份多样化、典型化、注释化的烹饪食谱数据集

Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning

Computational creativity is an emerging branch of artificial intelligence
that places computers in the center of the creative process. Broadly,
creativity involves a generative step to produce many ideas and a selective
step to determine the ones that are the best. Many previous attempts at
computational creativity, however, have not been able to achieve a valid
selective step. This work shows how bringing data sources from the creative
domain and from hedonic psychophysics together with big data analytics
techniques can overcome this shortcoming to yield a system that can produce
novel and high-quality creative artifacts. Our data-driven approach is
demonstrated through a computational creativity system for culinary recipes and
menus we developed and deployed, which can operate either autonomously or
semi-autonomously with human interaction. We also comment on the volume,
velocity, variety, and veracity of data in computational creativity.

数据驱动的计算机创意系统可结合创意领域和愉悦心理物理学数据来源，利用大数据分析技术产生新颖高质量的创意产物，本文以烹饪食谱和菜单为例展示了该方法。