Food Pairing Unveiled: Exploring Recipe Creation Dynamics through Recommender Systems

TL;DR

This paper investigates food pairing principles using recommender systems, confirming its existence but highlighting its limitations, and introduces novel tools to foster culinary innovation by leveraging ingredient similarities and flavor compounds.

Contribution

It applies advanced collaborative filtering techniques to analyze food pairing, compares recipe-based and flavor-based recommenders, and proposes new methods to enhance culinary creativity.

Findings

Recipe-based recommender outperforms flavor-based one.

Food pairing mainly results from similar ingredients.

Recommender tools can inspire new culinary scenarios.

Abstract

In the early 2000s, renowned chef Heston Blumenthal formulated his "food pairing" hypothesis, positing that if foods share many flavor compounds, then they tend to taste good when eaten together. In 2011, Ahn et al. conducted a study using a dataset of recipes, ingredients, and flavor compounds, finding that, in Western cuisine, ingredients in recipes often share more flavor compounds than expected by chance, indicating a natural tendency towards food pairing. Building upon Ahn's research, our work applies state-of-the-art collaborative filtering techniques to the dataset, providing a tool that can recommend new foods to add in recipes, retrieve missing ingredients and advise against certain combinations. We create our recommender in two ways, by taking into account ingredients appearances in recipes or shared flavor compounds between foods. While our analysis confirms the existence of food pairing, the recipe-based recommender performs significantly better than the flavor-based one, leading to the conclusion that food pairing is just one of the principles to take into account when creating recipes. Furthermore, and more interestingly, we find that food pairing in data is mostly due to trivial couplings of very similar ingredients, leading to a reconsideration of its current role in recipes, from being an already existing feature to a key to open up new scenarios in gastronomy. Our flavor-based recommender can thus leverage this novel concept and provide a new tool to lead culinary innovation.