  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.
