  With the booming of personalized recipe sharing networks (e.g., Yummly), a
deluge of recipes from different cuisines could be obtained easily. In this
paper, we aim to solve a problem which many home-cooks encounter when searching
for recipes online. Namely, finding recipes which best fit a handy set of
ingredients while at the same time follow healthy eating guidelines. This task
is especially difficult since the lions share of online recipes have been shown
to be unhealthy. In this paper we propose a novel framework named NutRec, which
models the interactions between ingredients and their proportions within
recipes for the purpose of offering healthy recommendation. Specifically,
NutRec consists of three main components: 1) using an embedding-based
ingredient predictor to predict the relevant ingredients with user-defined
initial ingredients, 2) predicting the amounts of the relevant ingredients with
a multi-layer perceptron-based network, 3) creating a healthy pseudo-recipe
with a list of ingredients and their amounts according to the nutritional
information and recommending the top similar recipes with the pseudo-recipe. We
conduct the experiments on two recipe datasets, including Allrecipes with
36,429 recipes and Yummly with 89,413 recipes, respectively. The empirical
results support the framework's intuition and showcase its ability to retrieve
healthier recipes.
