# A neural network system for transformation of regional cuisine style

arXiv: https://arxiv.org/abs/1705.03487 · HTML: https://mlsanity.com/paper/1705.03487/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2017.

## Built on by (most cited first, 4 of 4)

- [Learning to Substitute Ingredients in Recipes](https://mlsanity.com/paper/2302.07960/) (2023)
- [Retrieval Augmented Zero-Shot Text Classification](https://mlsanity.com/paper/2406.15241/) (2024)
- [A topological analysis of the space of recipes](https://mlsanity.com/paper/2406.09445/) (2024)
- [Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipes](https://mlsanity.com/paper/2409.08792/) (2024)

## Abstract (arXiv)

We propose a novel system which can transform a recipe into any selected regional style (e.g., Japanese, Mediterranean, or Italian). This system has two characteristics. First the system can identify the degree of regional cuisine style mixture of any selected recipe and visualize such regional cuisine style mixtures using barycentric Newton diagrams. Second, the system can suggest ingredient substitutions through an extended word2vec model, such that a recipe becomes more authentic for any selected regional cuisine style. Drawing on a large number of recipes from Yummly, an example shows how the proposed system can transform a traditional Japanese recipe, Sukiyaki, into French style.