Calorie Estimation in a Real-World Recipe Service

Abstract

Cooking recipes play an important role in promoting a healthy lifestyle, and a vast number of user-generated recipes are currently available on the Internet. Allied to this growth in the amount of information is an increase in the number of studies on the use of such data for recipe analysis, recipe generation, and recipe search. However, there have been few attempts to estimate the number of calories per serving in a recipe. This study considers this task and introduces two challenging subtasks: ingredient normalization and serving estimation. The ingredient normalization task aims to convert the ingredients written in a recipe (e.g.,), which says “sesame oil (for finishing)” in Japanese) into their canonical forms (e.g., , sesame oil) so that their calorific content can be looked up in an ingredient dictionary. The serving estimation task aims to convert the amount written in the recipe (e.g., N, N pieces) into the number of servings (e.g., M, M people), thus enabling the calories per serving to be calculated. We apply machine learning-based methods to these tasks and describe their practical deployment in Cookpad, the largest recipe service in the world. A series of experiments demonstrate that the performance of our methods is sufficient for use in real-world services.

Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach

“…Our study is the first to pay attention to noningredients in a user-generated recipe. This contributes to a variety of recipe-related studies, particularly based on ingredients in a recipe, such as calorie estimation (Harashima et al, 2020), recipe clustering (Nadamoto et al, 2016), and reciperelated term detection (Chung, 2012).…”