4.7 Article

Eating healthier: Exploring nutrition information for healthier recipe recommendation

Journal

INFORMATION PROCESSING & MANAGEMENT
Volume 57, Issue 6, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2019.05.012

Keywords

Recipe recommender system; Healthy recipes; Embedding learning; Neural network; Nutrition

Funding

  1. Fundamental Research Funds of Shandong University
  2. Natural Science Foundation of Shandong Province of China [ZR2019BF010]
  3. National Natural Science Foundation of China [61572289]
  4. NSERC

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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.

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