# A topological analysis of the space of recipes

arXiv: https://arxiv.org/abs/2406.09445 · HTML: https://mlsanity.com/paper/2406.09445/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 55 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists](https://mlsanity.com/paper/1710.04019/) (2017)
- [Ripser: efficient computation of Vietoris-Rips persistence barcodes](https://mlsanity.com/paper/1908.02518/) (2019)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [Food Recommendation: Framework, Existing Solutions and Challenges](https://mlsanity.com/paper/1905.06269/) (2019)
- [Volume Optimal Cycle: Tightest representative cycle of a generator on persistent homology](https://mlsanity.com/paper/1712.05103/) (2017)
- [A neural network system for transformation of regional cuisine style](https://mlsanity.com/paper/1705.03487/) (2017)
- [KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks](https://mlsanity.com/paper/1905.07261/) (2019)
- [Food-bridging: a new network construction to unveil the principles of cooking](https://mlsanity.com/paper/1704.03330/) (2017)
- [Flavor Pairing in Medieval European Cuisine: A Study in Cooking with Dirty Data](https://mlsanity.com/paper/1307.7982/) (2013)

## Built on by (most cited first, 1 of 1)

- [Topological Data Analysis Applications in Natural Language Processing: A Survey](https://mlsanity.com/paper/2411.10298/) (2024)

## Abstract (arXiv)

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.