A neural network system for transformation of regional cuisine style
Not on the map. Why?
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 May 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 3.
4 papers.
- 2023Learning to Substitute Ingredients in Recipescited by 8
- 2024Retrieval Augmented Zero-Shot Text Classificationcited by 4
- 2024A topological analysis of the space of recipescited by 1
- 2024Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipescited by 0
- 2024Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipes13 Sep 2024
- 2024Retrieval Augmented Zero-Shot Text Classification21 Jun 2024
- 2024A topological analysis of the space of recipes12 Jun 2024
- 2023Learning to Substitute Ingredients in Recipes15 Feb 2023