# Eating Healthier: Exploring Nutrition Information for Healthier Recipe Recommendation

arXiv: https://arxiv.org/abs/2003.07027 · HTML: https://mlsanity.com/paper/2003.07027/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://mlsanity.com/paper/2101.01775/) (2021)
- [SHARE: a System for Hierarchical Assistive Recipe Editing](https://mlsanity.com/paper/2105.08185/) (2021)
- [RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation](https://mlsanity.com/paper/2205.14005/) (2022)
- [RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer](https://mlsanity.com/paper/2210.10628/) (2022)

## Abstract (arXiv)

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.