A Large-scale Recipe and Meal Data Collection
as Infrastructure for Food Research
Jun Harashima, Michiaki Ariga, Kenta Murata, Masayuki Ioki
Cookpad Inc.
Yebisu Garden Place Tower 12F, 4-20-3 Ebisu, Shibuya-ku, Tokyo, 150-6012, Japan{jun-harashima, michiaki-ariga, kenta-murata, masayuki-ioki}@cookpad.com
Abstract
Everyday meals are an important part of our daily lives and, currently, there are many Internet sites that help us plan these meals. Allied
to the growth in the amount of food data such as recipes available on the Internet is an increase in the number of studies on these data,such as recipe analysis and recipe search. However, there are few publicly available resources for food research; those that do exist do
not include a wide range of food data or any meal data (that is, likely combinations of recipes). In this study, we construct a large-scale
recipe and meal data collection as the underlying infrastructure to promote food research. Our corpus consists of approximately 1.7
million recipes and 36000 meals in cookpad, one of the largest recipe sites in the world. We made the corpus available to researchers inFebruary 2015 and as of February 2016, 82 research groups at 56 universities have made use of it to enhance their studies.
Keywords: recipe, meal, food research

2.

1. Introduction
Nowadays many people plan meals using the Internet. Because meals need to be planned several times a day, this isa time-consuming and labor-intensive task. However, owing to the many Internet sites available to help users findrecipes and plan their meals (e.g., cookpad in Japanese,1and Yummly in English2 ), such planning has become mucheasier.
Alongside the increase in the amount of available fooddata such as recipes, there has been a corresponding increase in the number of studies on these data such as recipe
analysis (Kiddon et al., 2015; Jermsurawong and Habash,2015; Maeta et al., 2015; Sasada et al., 2015) and recipe
search (Yasukawa et al., 2014; Wang et al., 2008). Furthermore, many conferences and workshops have recently been
held to promote research on these topics.
Nevertheless, there are few publicly available resources forfood research. Additionally, such resources are not widely
applicable as they do not include a wide range of food data(a few dozen − hundreds of thousands of recipes) or any
meal data (data on likely combinations of recipes). Thus, anovel resource is required to promote food research.
In this study, we constructed a large-scale recipe and mealdata collection as the underlying infrastructure for food research. We organized approximately 1.7 million recipes,
36000 meals, and their related data (e.g., reviews) in cookpad and made the collected data available to researchers. In
this paper, we report on how we designed our corpus andhow this has contributed to enhancing food research.
The remainder of this paper is organized as follows. Section 2. briefly describes relevant previous studies. Sections 3. and 4. discuss, respectively, the recipe and mealdata in our corpus. Section 5. summarizes the contributions of this study while our conclusions are presented inSection 6..
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Related Work2.1. Large-scale Raw Data Collections
As described in the previous section, few resources exist
that include a wide range of food data. To the best of ourknowledge, the following corpora are the only two that include a reasonable amount of food data. Rakuten Data3 isa publicly available resource that includes approximately
440000 recipes written in Japanese, while approximately
100000 recipes in English extracted from Yummly were
published in the 11th NTCIR Workshop.4
We have constructed a novel corpus that is superior to theseresources in terms of the following aspects. The corpus
includes many more recipes than either of these existing
resources, while also providing meal data.

2.2. Small-scale Annotated Data Collections
Several resources with small amounts of data include manually annotated data. In the flow graph corpus (Mori etal., 2014), 266 Japanese recipes from cookpad are represented as graph structures, composed of vertices (namedentities) and arcs (representing relations between two entities). The Kyoto University Smart Kitchen Dataset selected 20 recipes from Mori’s corpus and linked cooking processes in each recipe to real human activities ina kitchen (Hashimoto et al., 2014). The Carnegie Mellon University Recipe Database (CMU Recipe Database)consists of 260 English recipes translated into a bespoke
machine-readable language (Tasse and Smith, 2008).
Some recipes and meals in our corpus have been manuallytagged with their categories (see the following sections).
Therefore, the corpus is similar to the above resources inthat all of these include annotated data. However, our corpus includes a vast amount of recipe and meal data and thus,
differs significantly from existing resources in terms of size.
3

cookpad.com
www.yummly.com

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http://rit.rakuten.co.jp/opendata.html
http://research.nii.ac.jp/ntcir/ntcir-11/index.html

2.3. Existing Studies on Food DataFirst, we introduce relevant studies focusing on the analysis of recipes. Kiddon et al. (2015) proposed an unsupervised method for extracting and identifying latent connections among actions in a recipe. Based on the expectationmaximization approach, the method defines what actions
should be performed on which objects (e.g., ingredients,tools) and in what order. Jermsurawong and Habash (2015)
represented a recipe as a dependency tree with the leaves
depicting ingredients in the recipe and internal nodes denoting instructions contained therein. They also built aparser that maps a recipe into their proposed tree structureand showed the accuracy of their parser using the CMU
Recipe Database. Maeta et al. (2015) proposed a framework for analyzing procedural texts, especially for recipes.
Their method tokenizes a recipe, recognizes concepts (e.g.,food, tools) like named entity recognition, and then connects these in a graph. Sasada et al. (2015) proposed a
named entity recognizer that can be trained from partiallyannotated data. They focused on the fact that fully annotated data is rarely available in food research and constructed the recognizer to overcome this problem. Nanba
et al. (2014) used both manual and automatic means toconstruct a cooking ontology for recipe analysis. Their resource comprises 474 entry words, 5023 synonyms, 1512
attributes, and 2429 meronymic words. Tachibana et al.
(2014) investigated identifying a naming concept from arecipe. As the concept, they extracted differences between
elements (e.g., ingredients, tools) of the recipe and typicalelements of other recipes for the same dish.
Studies that focus on applications for recipes, such as recipe
searches, recipe recommendations, and recipe summarizations, are discussed below. Yasukawa et al. (2014) implemented a search task, called RecipeSearch,5 to study information access for recipe data in the 11th NTCIR Workshop.
To implement the task, they used Rakuten and Yummly
data, as described in the previous section, and four researchgroups participated in the task. Yamakata et al. (2013) focused on extracting a general way of cooking as a summaryof cooking procedures. Their approach converts recipes
into graph structures and finds common structures in theseto form the summary. Forbes and Zhu (2011) proposed arecommendation method for recipes based on matrix factorization. They incorporated content information in a recipe(i.e., ingredients) into their approach and confirmed the usefulness of the method through various experiments. Wanget al. (2008) constructed a similarity search system for Chinese recipes. The system translates recipes into directed
graphs and for a given query, displays a recipe together withother similar ones based on the graph structures.
All of the above studies use some food data. However,researchers often need to implement a crawler themselvesto obtain the data from recipe sites. We have made ourcorpus available to researchers with the aim of facilitating
the study of food data. Additionally, by creating a common infrastructure for this field, our corpus should assist
researchers in discussing their studies with others.
5

https://sites.google.com/site/ntcir11recipesearch/

Figure 1: A recipe template in cookpad.

3. Recipe Data
As at February 2016, the cookpad comprised over 2.2 million recipes written in Japanese, making it one of the largest
recipe sites in the world. On this site, users can upload theirrecipes using the template shown in Figure 1.
We organized the recipes and their related data in cookpad to help researchers use them. First, we collected approximately 1.7 million recipes that had been uploaded to
cookpad by September 2014. Figure 2 gives an exampleof recipes in cookpad with the main information provided
by us, demarcated by orange rectangles. In this study, weincluded text information, but no image information (i.e.,a photo for each recipe); this has been left for our futurework. Details of the text information fields added are givenbelow.
A. Title
The title, consisting of a maximum of 20 characters, gives
a concise summary of the recipe. For example, the title inFigure 2 is 豚のにんにく醤油焼き (pork with garlic soysauce).
B. Description
Using this data field, authors can provide an eye-catchingdescription of their recipes (e.g., “this goes well with rice”
as shown in Figure 2). This text is also shown as a snippet
in the recipe search results in cookpad.
C. Ingredients
Ingredients used in the recipe (e.g., pork in Figure 2) arelisted in this field together with the quantities thereof. Thisdata field also describes how many servings the recipe provides.

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Figure 2: An example of a recipe from cookpad.

D. Steps

Figure 3: An example of a meal from cookpad.

This field describes the method of cooking. A recipe usually consists of multiple steps (e.g., four steps in Figure 2)
all of which are explained in detail in this data field.
E. Advice and Points to Note
In this field, authors can provide additional hints about theirrecipe to help readers prepare the dish. An author suggeststhat Japanese ginger and green perilla can be used insteadof chives in the recipe depicted in Figure 2.
F. History
This field explains how and why the recipe was created. For
example, the history field in Figure 2 states that the recipe
was devised for the author’s husband, who likes garlic.
Although Figure 2 only shows the main information for therecipe, our corpus also includes the following data for each
recipe: the unique ID of the recipe, the unique ID of theauthor, and the date when the recipe was uploaded.
In addition, we provide researchers with related data foreach recipe. In cookpad, more than 146000 recipes havebeen classified into approximately 1100 categories by users(e.g., meat dishes, seafood dishes, vegetable dishes). Moreover, there are almost 10 million reviews of recipes (called“tsukurepo”) in cookpad, which are also included in ourcorpus.

4. Meal Data
Unlike the existing resources, our corpus includes not
only recipes but also meals (that is, likely combination ofrecipes). In addition to the recipes, we collected approximately 36000 meals in cookpad that had been uploaded bySeptember 2014. Similar to recipes, meals are uploaded using the template provided by cookpad. Figure 3 shows anexample of a meal, with the main information for the meal
provided by us, explained below.
G. Title
The title, consisting of a maximum of 20 characters, gives
a concise summary of the meal. For example, the title inFigure 3 is ドライカレープレート (curried pilaf).
H. Noteworthy Points
Similar to the advice field in recipes, this field explains various aspects of the respective meal to help readers prepare
it. In Figure 3, the author states that a salad was added as aside dish to balance the flavors.
I. Cooking Time
This field states how long it took the author to prepare themeal (e.g., 40 min in Figure 3). When an author submits
his/her meal, the total preparation time in minutes must be
selected from the given options.

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Our data
Rakuten data
Yummly data
(Mori et al., 2014)(Hashimoto et al., 2014)(Tasse and Smith, 2008)

language
JapaneseJapaneseEnglishJapaneseJapaneseEnglish# of recipes
approx. 1715000
approx. 440000
approx. 100000
266
20260

# of meals
approx. 36000
N/A
N/A
N/AN/A
N/A

Table 1: Statistics of our data and existing data.

J. Advice
A meal usually consists of multiple dishes, some of which
may be difficult to prepare simultaneously. This field provides various hints about the meal to help readers prepare
multiple dishes efficiently.
K. Main Dishes
Main dishes for the meal, of which there can be many, arelisted in this field. The recipe data for the dishes are also
included in our corpus (see the previous section).
L. Side Dishes
Side dishes for the meal are listed in this field. As in thecase of main dishes, there can be multiple side dishes, therecipe data for which are also included in our corpus.
Our corpus also includes the following data for each meal:
the unique ID of the meal, the unique ID of the author, andthe date when the meal was uploaded.
Moreover, we provide researchers with related data for eachmeal. In cookpad, meals are classified into categories (e.g.,Japanese style, Western style) and some meals are voted
for by users. Such user-created data are also included inour corpus.

5.

Data Release

We have made our corpus available in cooperation with
the National Institute of Informatics (NII),6 a Japanese Research Institute with the goal of advancing informatics research. The NII aggregates information on various kindsof datasets for informatics research. Our corpus, which isavailable from a site administered by the Institute,7 is distributed as a MySQL dump, consisting of 12 tables: six forrecipe data and six for meal data. Any researcher in public institutions such as universities can obtain access to thecorpus if he/she wishes to use it for research purposes.
In Table 1, we summarize the statistics of our corpus andothers described in Section 2.. From the table, it is clearthat our corpus is the biggest in the world.
Between its release in February 2015 and February 2016,
our corpus has already been acquired by 82 research groups
at 56 universities. With the availability of our corpus, there
is no need for individual researchers to implement crawlers
themselves, as described in Section 2.. Additionally, thecorpus makes it easier for researchers to compare their studies with those of others. As described above, our corpus
forms the basic infrastructure for food research and we67

http://www.nii.ac.jp/
http://www.nii.ac.jp/dsc/idr/cookpad/cookpad.html

strongly believe that the corpus will enhance research inthis field.

6.

Conclusion

In this study, we constructed a novel language resource topromote food research. Our corpus includes more than 1.7
million Japanese recipes taken from cookpad. Unlike previous resources, it also includes data for approximately 36000
meals. Since making the corpus available for research purposes, researchers at 56 universities have used it in theirstudies.
In future, as described in Section 3., we plan to provide
image information to promote image recognition research.
Additionally, we plan to provide a comparable recipe corpus, consisting of Japanese recipes with their English translations, to contribute to machine translation research.

Acknowledgments
We gratefully acknowledge the support of the National Institute of Informatics.

7. Bibliographical References
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Factorization for Recommender Systems: Experimentswith Recipe Recommendation. In Proceedings of the5th ACM Conference on Recommender Systems (RecSys2011), pages 261–264.
Hashimoto, A., Sasada, T., Yamakata, Y., Mori, S., and Minoh, M. (2014). KUSK Dataset: Toward a Direct Understanding of Recipe Text and Human Cooking Activity. InProceedings of the Workshop on Smart Technology forCooking Eating Activities (CEA 2014), pages 583–588.
Jermsurawong, J. and Habash, N. (2015). Predicting theStructure of Cooking Recipes. In Proceedings of the2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), pages 781–786.
Kiddon, C., Ponnuraj, G. T., Zettlemoyer, L., and Choi, Y.
(2015). Mise en Place: Unsupervised Interpretation ofInstructional Recipes. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), pages 982–992.
Maeta, H., Sasada, T., and Mori, S. (2015). A Frameworkfor Procedural Text Understanding. In Proceedings ofthe 14th International Conference on Parsing Technologies (IWPT 2015), pages 50–60.
Mori, S., Maeta, H., Yamakata, Y., and Sasada, T. (2014).
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