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From pizza to curry: preferences for recipes around the world

Published: 29 October 2019 Publication History

Abstract

A considerable part of the culture and behavior of societies are derived from the habits and preferences built up over time. One representative characteristic that a group can present is the preference for certain food groups, thus, building the gastronomic identity of each region around the world. With the increasingly broad connections established by social networks, it is now more feasible to analyze such preferences on a large scale. This study examines recipes from Allrecipes.com network in three continents: America, Europe, and Asia. Based on the evaluations made by the users, a score was developed, allowing the separation of the recipes in two broad groups: well evaluated and poorly evaluated. All the ingredients of these recipes were extracted and used to assemble a network whose links were made via pointwise mutual information. This measure of association, used in pairs of ingredients, allowed us to find the main ingredients common to the countries. Our study may help to better understand the success, or otherwise, of a recipe, in a specific locality, based on its main ingredients. Thus, one of the main utilities envisioned for this work is to establish better recommendations for recipes.

References

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C. Wagner and L. M. Aiello, "Men eat on mars, women on venus?: An empirical study of food-images," in Proceedings of the ACM Web Science Conference, (Oxford, UK), p. 63, ACM, 2015.
[2]
A. Salvador, N. Hynes, Y. Aytar, J. Marin, F. Ofli, I. Weber, and A. Torralba, "Learning cross-modal embeddings for cooking recipes and food images," Training, vol. 720, no. 619-508, p. 2, 2017.
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Y.-Y. Ahn, S. E. Ahnert, J. P. Bagrow, and A.-L. Barabási, "Flavor network and the principles of food pairing," Scientific Reports, vol. 1, pp. 196 EP -, Dec 2011. Article.
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C.-Y. Teng, Y.-R. Lin, and L. A. Adamic, "Recipe recommendation using ingredient networks," in Proceedings of the 4th Annual ACM Web Science Conference, WebSci '12, (New York, NY, USA), pp. 298--307, ACM, 2012.

Cited By

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  • (2020)Collaborative Filtering Strategy for Product Recommendation Using Personality Characteristics of CustomersProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3430969(157-164)Online publication date: 25-Nov-2020

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Published In

cover image ACM Other conferences
WebMedia '19: Proceedings of the 25th Brazillian Symposium on Multimedia and the Web
October 2019
537 pages
ISBN:9781450367639
DOI:10.1145/3323503
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2019

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Author Tags

  1. allrecipes
  2. complex networks
  3. food
  4. recipes

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  • Short-paper

Funding Sources

  • CNPq-UrbComp

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WebMedia '19
WebMedia '19: Brazilian Symposium on Multimedia and the Web
October 29 - November 1, 2019
Rio de Janeiro, Brazil

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Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

View all
  • (2020)Collaborative Filtering Strategy for Product Recommendation Using Personality Characteristics of CustomersProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3430969(157-164)Online publication date: 25-Nov-2020

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