A neural network system for transformation of regional cuisine style

Masahiro Kazama, Minami Sugimoto, Chizuru Hosokawa, Keisuke Matsushima, Lav R. Varshney, Yoshiki Ishikawa

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number14
JournalFrontiers in ICT
Volume5
Issue numberJUL
DOIs
StatePublished - 2018

Keywords

  • Big data
  • Food
  • Neural network
  • Newton diagram
  • Regional cuisine style
  • Word2vec

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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