Analyzing Dynamical Activities of Co-occurrence Patterns for Cooking Ingredients

Kikuchi, Yuuki; Kumano, Masahito; Kimura, Masahiro

doi:10.1109/icdmw.2017.10

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…A recent extensive survey preforms such an investigation with the aim of characterizing different cuisines [1]. Kikuchi et al [22] demonstrate the effects of seasonality on recipes' content and illustrate how ingredient co-appearance in recipes changes throughout a year. In [23] the typicality of ingredients within a recipe category (e.g.…”

Section: Recipe Completionmentioning

confidence: 99%

Eating healthier: Exploring nutrition information for healthier recipe recommendation

Chen

Jia

Gorbonos

et al. 2020

Information Processing & Management

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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 perceptronbased 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 * This is to indicate the corresponding authors.intuition and showcase its ability to retrieve healthier recipes.

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Section: Recipe Completionmentioning

confidence: 99%

Eating healthier: Exploring nutrition information for healthier recipe recommendation

Chen

Jia

Gorbonos

et al. 2020

Information Processing & Management

View full text Add to dashboard Cite

show abstract

“…A major challenge with recipe datasets is that no standard online collections exist yet due to restrictions in terms of use, language, etc. Previous research often uses proprietary materials through APIs including crawls from online recipe collections and databases such as epicurious.com , allrecipes.com , RecipeDB, CulinaryDB, Hawa World, TarlaDalal.com , and chefkoch.de ( Shidochi et al, 2009 ; Ahn et al, 2011 ; Teng et al, 2012 ; Ahnert, 2013 ; Jain et al, 2015 ; Kusmierczyk et al, 2015 ; Bogojeska et al, 2016 ; Tallab and Alrazgan, 2016 ; Kikuchi et al, 2017 ; Sajadmanesh et al, 2017 ; Bagler and Singh, 2018 ; Chang et al, 2018 ; Min et al, 2018 ; Asano and Biermann, 2019 ; Batra et al, 2019 ; Trattner and Elsweiler, 2019 ; Herrera, 2020 ; Sharma et al, 2020 ). To be useful in practical applications, these untapped sources require structuring, linking, and analysis via NLP techniques.…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

Erp¹,

Reynolds²,

Maynard³

et al. 2021

Front. Artif. Intell.

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In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

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Analyzing Textual Sources Attributes of Comics Based on Word Frequency and Meaning

Higuchi

Yamanishi

Matsushita

2023

Lecture Notes in Computer Science

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Analyzing Dynamical Activities of Co-occurrence Patterns for Cooking Ingredients

Cited by 6 publications

References 17 publications

Eating healthier: Exploring nutrition information for healthier recipe recommendation

Eating healthier: Exploring nutrition information for healthier recipe recommendation

Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

Analyzing Textual Sources Attributes of Comics Based on Word Frequency and Meaning

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