A Cooking Recipe Multi-Label Classification Approach for Food Restriction Identification

  • Larissa Britto Universidade Federal Rural de Pernambuco
  • Luciano Pacífico Universidade Federal Rural de Pernambuco
  • Emilia Oliveira Universidade Federal Rural de Pernambuco
  • Teresa Ludermir Universidade Federal de Pernambuco


Recipe sharing websites have become very popular in the past years, allowing individuals to use such systems in an attempt to find a desired recipe. But sometimes finding recipes which best fit the user's wishes, while still satisfying his food restrictions, may become a very time consuming and difficult task. In this work, we propose a recipe multi-label classification approach as part of a recipe recommendation system for people with food restrictions, in an attempt to automatically identify whether an input recipe or list of ingredients fits into one or more food restrictions, satisfying both user's expectations and needs. The experimental evaluation includes two approaches for feature selection, as a manner to reduce the computational costs for the proposed system.

Palavras-chave: Cooking Recipe Classification, Text Analysis, Machine Learning, Food Restriction


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BRITTO, Larissa; PACÍFICO, Luciano; OLIVEIRA, Emilia; LUDERMIR, Teresa. A Cooking Recipe Multi-Label Classification Approach for Food Restriction Identification. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 246-257. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2020.12133.

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