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.

随着个性化食谱共享网络（例如Yummly）的蓬勃发展，可以轻松获得大量来自不同美食的食谱。在本文中，我们旨在解决许多家庭厨师在在线搜索食谱时遇到的问题。即，找到最适合方便使用的食材的食谱，同时遵循健康饮食准则。由于已证明大部分在线食谱都不健康，因此此任务特别困难。在本文中，我们提出了一个名为NutRec的新颖框架，该框架可以对配方中成分及其比例之间的相互作用进行建模，以提供健康的推荐。具体来说，NutRec由三个主要成分组成：1）使用基于嵌入的成分预测器来预测具有用户定义的初始成分的相关成分，2）通过基于多层感知器的网络预测相关成分的数量，3）根据营养信息创建包含成分及其含量的清单的健康假食谱，并使用该假食谱推荐最相似的顶级食谱食谱。我们在两个配方数据集上进行了实验，包括分别具有36,429个配方的Allrecipes和具有89,413个配方的Yummly。实证结果支持该框架的直觉，并展示了其检索更健康食谱的能力。我们在两个配方数据集上进行了实验，包括分别具有36,429个配方的Allrecipes和具有89,413个配方的Yummly。实证结果支持该框架的直觉，并展示了其检索更健康食谱的能力。我们在两个配方数据集上进行了实验，包括分别具有36,429个配方的Allrecipes和具有89,413个配方的Yummly。实证结果支持该框架的直觉，并展示了其检索更健康食谱的能力。