Clustering for Closely Similar Recipes to Extract Spam
Recipes in User-generated Recipe Sites
Shunsuke Hanai

Hidetsugu Nanba

Akiyo Nadamoto

Konan University
Okamoto8-9-1
Higashinada-ku Kobe, Japan

Hiroshima City University
Ozukahigashi 3-4-1
Hiroshima 731-3194 Japan

Konan University
Okamoto8-9-1
Higashinada-ku Kobe, Japan

m1424007@center.konanu.ac.jp

nanba@hiroshimacu.ac.jp

nadamoto@konanu.ac.jp

ABSTRACT

Many user-generated recipe sites are accessible on the in-

Nowadays, many user-generated recipe sites are accessibleon the internet.

On user-generated recipe sites, however,are various spam recipe pages that describe closely similar
recipes requiring special cooking equipment, with no preparation explanations. These spam recipes are not useful forusers. In fact, they impede user's recipe searches. In thispaper, we target closely similar recipes as a rst step in extracting spam recipes. If user search results could be classied to identify closely similar recipes, user's recipe searches
would be easier and more productive.

Clustering tools of

many kinds are proposed, but it is dicult to cluster closely
similar recipes using only existing clustering tools because
recipe sites have a unique page structure comprising a title, ingredients, directions (preparation instructions), andcomments. The importance of words from each part diers.
We propose a clustering method for user-generated recipesites based on page structure and important words. Next,
we conducted an experiment to measure the benets of our
proposed method.

The result of experiment presents thebenets of our proposed method which classify the closely
similar recipes.

1

2

ternet today, such as Food.com (U.S.), Mis Recetas (Hispanic),

34

Beitai Chufang (China) and Cookpad (Japan). These recipesites have many users who post their recipes freely, providing recipes of many kinds for themes such as home cooking,party cooking, and diet foods. Many users also search forrecipes for their meals from the recipe sites. For example,
the number of monthly users of Cookpad is approximately
44,930,000 (July, 2014). The number of recipe pages is about
2,120,000 (August 2015).
When a user searches for a recipe, the user poses queries of
two types, typically incorporating food names such as beef
stew or lasagna, and ingredient names such as chicken, cabbage, or onions.

Maruha-Nichiro Holdings, a famous foodcompany in Japan, investigated which query is better used
when users seek recipes posted on recipe sites [8]. In fact, ingredient keywords are used more often than food name keywords. Ingredient names account for 75% of all keywords.
Results show that users who search for recipes before deciding a menu account for about 57% of all users. Users who
search for a recipe after they have chosen a menu are about
26% of all users. Therefore, when users use recipe sites, they
input an ingredient name as a query. However, when users

Categories and Subject Descriptors
H.4.m [Information Systems]: Miscellaneous; I.7.m [Document and Text Processing]: Miscellaneous

search for a recipe using an ingredient name, they are often deluged by inconvenient recipes of many kinds that areclosely similar recipes, but which use special cooking equipment, and which include no preparation explanation whatsoever. These inconvenient recipes are not useful for users.

General Terms
Algorithms

Actually, they impede user's recipe searches. These closely
similar recipes obstruct user searches because of their sheer
number on user-generated recipe sites.

They are, in fact,

spam recipes. We regard extraction of these spam recipes as

Keywords
User-generated recipe, Clustering, Closely similar recipes

an important and valuable service. Especially, deliberately
or accidentally, numerous closely similar recipes are posted
among the user-generated recipes.

For example, when auser inputs Tomato and Chicken in Cookpad, a famous

1. INTRODUCTION

Japanese user-generated recipe site, the results extend to
more than 10,000 pages.

Numerous closely similar pages

are found. That information overload" confuses users.

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Moreover, a user who searches for a recipe usually does
not select a high-ranking recipe from the search results, re-1
Food.com http://www.food.com/
2
Mis Recetas http://www.misrecetas.com/
3
Beitai chufang http://www.beitaichufang.com/
4
Cookpad http://cookpad.com/

acting better than one might with usual web searches [10].

Experimental results demonstrated that the algorithm not

Data show that users typically compare multiple recipes.

only improves the recommendation accuracy; it is also use-

Given many similar recipes included in the search results, it

ful for swapping ingredients and creating new recipes. These

would be dicult to compare multiple recipes. When users

studies have focused on ingredients. However, these studies

compare similar recipes, they must better understand thespecically examined substitute ingredients and substitutedierent points of similar recipes. That need for compari-recipe recommendation systems:

son imposes a great burden on users. A system classifying

recipes is not the goal.

the extraction of similar

the results of user searches according to similar pages in realtime would be benecial for users. As described herein, we
propose a method that classies the user search results according to closely similar pages based on the page structure
and the types of important words. Clustering tools of many

3.

CLOSELY SIMILAR RECIPE CLUSTERING
From our previous study[3][4], we revealed the following

kinds have been proposed, but it is dicult to classify closely

four points for the clustering of closely similar recipes.

similar recipes using existing clustering tools because recipe• No image is necessary to judge recipe similarity.

sites have a unique page structure that includes the title of adish, with ingredients, directions (preparation instructions),
and comments.

These parts of pages dier in their roles,

importance, and meaning.

As described in this paper, we

propose a means of classifying the results of a user searchaccording to closely similar pages. Next, we conducted anexperiment to assess the benets of our proposed method.

• Food names, ingredients, seasonings, and cooking methods in a title are important words.

Especially, users

care about food names and cooking methods in thetitle when judging closely similar recipes.

• Main ingredients and the main seasoning in an ingredient list are important.

The remainder of this paper is organized as explained below. First, Section 2 presents a summary of related works.
Section 3 presents a method for clustering the spam recipes.
Section 4 presents discussion related to experimental evaluation, along with evaluation results.

Finally, Section 5

presents conclusions and expectations for future work.

• Sizzle words in a title are not necessary to judge therecipe similarity.
We propose clustering methods based on four points to
extract closely similar recipes.

The ow of clustering for

closely similar recipes is the following:
1. The system searches the recipe pages using multiple

2. RELATED WORK
Along with the growth of recipe-sharing services, manystudies have examined recipe recommendation processes. Wagner et al. [14] proposed a system that tracks a user's cooking

ingredients from the user input.
2. It extracts the title and ingredient list from search results.

activities with sensors in kitchen utensils and which recommends healthy recipes that might increase the user's cooking
competence.

Svensson et al.

[11] applied their idea of so-cial navigation for recipe recommendation. Specically, they
assigned users to groups based on their explicit preference
information such as ingredients, fat level, and time to cook.
Geleijnse et al. [2] designed a prototype of a personalized
recipe recommendation system, which suggests recipes tousers based on their past food selections and nutrition intake. Lawrence et al. [7] proposed a product recommendation system for grocery shopping using collaborative ltering

3. It extracts nouns from the results of (2) and performs
classication by food name, cooking method, ingredient, and seasoning, depending on our food database.
4. It classies recipe pages according to the kind of foodbased on the food name and cooking method in title.
Here, we use Repeated Bisection method[15] as a clustering method.
5. It calculates feature values of ingredients and seasonings in titles and ingredient lists.

to rate products based on the user's prior purchase behavior. Harvey et al. [5] explained factors of selecting recipes

6. It classies results of (4) according to closely similar

by assessing the results of long-term research to recommend

recipes using feature values of ingredients and season-

recipes that match a user's preferences. However, we par-ings in the titles and ingredient lists.

ticularly examine recipe similarity, not personalization. We

we use words without user input keywords because in-aimed to be helpful for user's search recipes by extracting

put keywords certainly include all recipe pages. The

spam recipes.

keywords are not regarded as characteristic words. If

At this time,

the keywords include clustering-based words, then theTeng et al. [12] proposed ingredient recommendation sys-

keywords might become central words of a cluster. We

tems using ingredient networks. They constructed networks

can not extract closely similar recipes. Therefore, weof two types to capture the relations among ingredients: in-delete the keywords when classifying the recipes.

gredient complements and substitute ingredients. Pinxteren
et al. [13] identied important features and extracted themfrom the recipe texts. They calculated the degree of similar-

3.1 First Clustering Based on Food Names andCooking Methods in the Title

ity among recipes, and changed to healthy recipes. Shidochi

Our previous study demonstrated that the food name andet al. [9] proposed a method to identify replaceable ingre-

cooking method in the title is the most important element

dients by matching the cooking actions that correspond toto judge closely similar recipe. We rst classify recipe pages

ingredient names from recipe texts.

based on food names and cooking methods in the title.

Forbes et al.[1] apply

a matrix factorization method to recipe recommendation.

The target data are search results of recipe pages using

Table 1: Condition of Experiment 2.

user input multiple ingredient such as pork and onion, andchicken and tomato.

Query

We extract titles. Next, we extractfood names and cooking methods using our food database,

Pork ∩ Egg Plant

which includes food names and cooking methods. When we

Pork ∩ Onion

classify recipe pages, we use Repeated Bisection [15] which
is a method used for bayon

5

and CLUTO

6

Pork ∩ Radish

Chicken ∩ Potato; it is a kind of

Avocado ∩ Tomato

K-means method. After the rst clustering, the recipe pages

Number of recipe

Number of cluster

pages

in clustering

5,885

135

28,525

230

8,446

146

9,147

142

5,284

98

are clustered by food names or cooking methods included in
the title area.

3.2

Second Clustering Based on Ingredientsand Seasoning

The rst clustering uses only the food name and cooking
method. There are many dierent meals but they are in thesame cluster. For example, one cluster is related to curry,

We measure the accuracy of our proposed method. The
datasets are shown in Table 1. We discuss the results divided
into the rst clustering and second clustering.

4.1

which is a food name. The elements of the cluster include
Vegetable curry, Tomato curry, Pork curry, Soft pork
curry, Chicken curry, and Chicken spicy curry.

They

are not closely similar recipes. Next, we classify them again
based on ingredients and cooking methods.

First, we classify recipes based on the food name and cooking method. Table 2 presents examples of the results for eachof the top three clusters. The results demonstrated that wecan nd the clusters are divided into the food name or cooking method. However, we can nd that a cluster divided into

Our previous study demonstrates that words in a title are
more important than other words in a passage.

First Clustering Based on Food Name andCooking Method

Calculat-ing the weight of words is necessary based on their positionof appearance on a page. Furthermore, a surprising degreeof ingredients in a food name is important because unusual
ingredients for the food should be regarded as recipe characteristics. For example, for the recipe title Lasagna, some
ingredients are tomato, pork, onion, cheese, and tofu.

In

this case, the tofu is an unusual lasagna ingredient. There-

food or cooking methods includes various main ingredientsor seasonings. For example, the query of Pork and Onion
in the rst cluster includes vegetable curry, tuna curry, andpork curry. In this case, the cluster is divided into the foodname curry, but the main ingredients dier; they are notclosely similar recipes. Therefore, we must classify smallerclusters using ingredients and seasonings.

4.2

fore, that unusual ingredient becomes a recipe characteris-

Second Clustering Based on Ingredient andSeasoningtic. Then we calculate the surprising degree of ingredients

Next, we classify the results of the rst clustering basedusing our surprising degree S-RF-IIF which is based on theon ingredients and seasonings by considering the appear-

Recipe Frequency-Inverted Ingredient Frequency (RF_IIF)

ance points. Table 3 presents examples of the results of the[6].

S_RF_IIF calculates a value reecting the degree of

top three clusters of each cluster 1 in Table 2. The results

surprise which might be associated with ingredient inclu-

demonstrate that the same food names with dierent mainsion based on the term frequency and the term-appearance

ingredients are classied into dierent clusters. For exam-

position. Expression of S _RF _IIFi,m as follows:

ple, in the rst clustering at query for Pork and Onion,

|Rm |
|Rm |
S _RF _IIFi,m = α log+ β log|Ri,t,m ||Ri,o,m |the dierent main ingredient in the same cluster, but as a
(1)

The given i is an ingredient name and m is a food name.

In addition, |Rm | denotes the number of recipes of food m,|Ri,t,m | denotes the number of recipes of food m, which includes ingredient i in title of food m. |Ri,o,m | denotes thenumber of recipes of food m, which includes ingredient iin passages except title of recipe page of food m. In that
equation, α signies a weight that is dependent on a term's
appearance position in title, and β signies a weight thatis dependent on a term's appearance position in ingredientlist. In this paper, we regard α = 1.0 and β = 0.5 by experiment of parameter determination.
Then we classify the results of the rst clustering using Repeated Bisection based on ingredients and seasonings, which
are feature values calculated using S-RF-IIF.

result of the second clustering, these recipes are classiedinto dierent clusters. Almost all closely similar recipes areclassied into the same cluster.

We can then classify theclosely similar recipes using our proposed method. We discuss the precision of the results. The results of precision are
dispersion (see Figure 1). In Figure 1, the horizontal axis
presents the values of precision. The vertical axis shows thenumber of clusters. The value of precision having the greatest number of clusters is 0.6 in the ve graphs.

However,the value of precision is 0. It has a large number of clusters
for two reasons: (1) general seasonings, which are water andsalt, become the center words of the cluster; (2) general ingredients of a dish, such as carrots and potatoes in curry,
become center words. We should consider the two problems
explained above.

5.

CONCLUSION
This paper proposed a clustering method to extract closely

4. EXPERIMENTS

similar recipes in user generated recipe sites such as Food.com,

5
Bayon https://code.google.com/p/bayon/wiki/Tutorial_en
6method is twice clustering, with specic examination of the

CLUTO http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview

Mis Recetas, Beitai Chufang, and Cookpad. Our proposed
page structure and types of important words. The rst clustering classies user search results based on the food nameTable 2: Results of First Clustering

Query : Pork AND Eggplant
Cluster1, Dish name: Stir - fry
Recipe title
Eggplant miso stir-fry
Eggplant stir-fry with miso
Meat and eggplant stir-fry with oyster sauce
Chopped pork and eggplant stir-fry of thick and delicious
Simply! Pork belly stir-fry with gochujang
Query : Pork AND Onion
Cluster1, Dish name: Curry
Recipe title
Curry with plenty of summer vegetables
Japanese-style tuna curry
Curry avor of pork and apple
Low price! Pork belly and cartilage curry
Pork curry of Larger vegetables
Query : Pork AND Radish
Cluster1, Dish name: Simmered
Recipe title
Simmered radish and ground meat
Simmered pork
Chinese-style simmered pork and root vegetables
Simmered radish in a pressure cooker
Lightly simmered radish
Query : Chicken AND Potato
Cluster1, Dish name: Stew
Recipe title
Tomato stew with chicken meatball
Tomato stew with rich avor and sweet chicken
Chicken and vegetable stew
Cream stew with soy milk
Cream stew
Query : Avocado AND Tomato
Cluster1, Dish name: Salad
Recipe title
Tuna and avocado salad
Tuna and avocado salad
Basil avor salad with avocado and mozzarella
Avocado and tuna salad
Japanese style salad of avocado and cream cheese

Cluster2, Dish name: Saute
Recipe title
Pork ginger saute
Pork saute with mustard avor
Simply! Pork saute with special sauce
Pork saute!! Balsamic sauce
Pork saute with spinach cream sauce

Cluster3, Dish name: Pasta
Recipe title
Tomato pasta with eggplant and ground meat
Eggplant meat pasta
Pasta with bitter melon, eggplant, and ground meat
Tomato pasta with eggplant and ground meat
Tomato cream pasta with milk

Cluster2, Dish name: Roll
Recipe title
Plenty of veggies! Basic spring rollSpring roll at home
Basic spring roll!!
Made at home! Home-made spring roll
Mini spring roll

Cluster3, Dish name: Meatloaf
Recipe title
Healthy! Meatloaf
Made with ground meat! Meatloaf
For a party! Juicy meat loaf
Made in the microwave!! Meatloaf
Cheese meat loaf with celery and raisins

Cluster2, Dish name: Marinade
Recipe title
Marinated Pork &Veggies for a feast
Refreshing taste! Marinated pork
Marinated thinly sliced pork meat
Pork marinated in pickled plum & miso
Easy Japanese-style marinated pork & radish

Cluster3, Dish name: Sukiyaki
Recipe title
Pork kimchi sukiyaki
Not a beef! Pork sukiyaki
For Beauty! Healthy sukiyaki bowl
Pork sukiyaki with radish and egg
Pork sukiyaki part2! Easy!

Cluster2, Dish name: Risotto
Recipe title
Tomato risotto
Very easy!! Baked cheese curry risotto
Chicken thigh and seasonal vegetables risotto
Potato risotto
Spring!! New potatoes and chicken risotto

Cluster3, Dish name: Soup
Recipe title
Potato potage soup
Easy potato and enoki mushroom potage soup
Burdock potage soup
Plenty of veggies! Potage soupSpring cabbage potage soup

Cluster2, Dish name: Sandwich
Recipe title
Avocado sandwich
Avocado and egg sandwich
Avocado and Tomato! Veggie sandwich
For diet! Avocado sandwich
Sandwich with fresh vegetables

Cluster3, Dish name: OmeletteRecipe title
Avocado omeletteOmelette(avocado & tomato)Fluy omelette with avocado and chicken
Italian-style omelette with avocado
Healthy! Veggie omelette

6. ACKNOWLEDGMENTS
This work was partially supported by JSPS KAKENHI
grant number 26330347. In this paper, we used recipe data
provided by Cookpad and the National Institute of Informatics.

7. REFERENCES[1] P. Forbes and M. Zhu. Content-boosted matrix
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In the near future, we expect to consider the cooking ow[4] S. Hanai, H. Nanba, and A. Nadamoto. Clustering forin addition to the values of ingredients and seasonings toclosely similar recipes to extract spam recipe. In IPSJ

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Table 3: Result of Second Clustering

Query : Pork AND Eggplant in cluster1 of table2
Cluster1
Recipe title
Pork & eggplant stir-fry with gochujang
Thickly stir-fry with eggplant and pork
Gochujang stir-fry with eggplant and pork
Rich stir-fry with pork and eggplant
Simply! Pork belly stir-fry with gochujang
Query : Pork AND Onion in cluster1 of tabel2
Cluster1
Recipe title
Thickly! Pork cartilage curry
Low price! Pork belly and cartilage curry
Delicious home made! Pork cartilage curry
Delicious curry with cartilage and lotus root
Collagen rich pork cartilage curry
Query : Pork AND Radish in cluster1 of table2
Cluster1
Recipe title
Addictive chewy! Simmered pig cartilage and radish
Simmered pig cartilage and radish
Simmered radish and pig cartilage
Melting! Simmered pig cartilage and vegetables
Simmered pig cartilage
Query : Chicken AND Potato in cluster1 of table2
Cluster1
Recipe title
Chicken soy milk stew
Stew using soy milk
Cream stew with plenty of veggies and soy milk
Soy milk cream stew
Soy milk stew
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Cluster1
Recipe title
Tuna and avocado salad
Tuna and avocado saladTuna, tomato and avocado salad
Plenty of vegetables! Tuna and avocado salad
Tuna & avocado salad with wasabi soy sauce

Cluster2
Recipe title
Eggplant and pork stir-fried in oyster sauce
Eggplant and pork belly stir-fried in oyster sauce
Oyster sauce stir-fried with eggplant, green pepper
Eggplant and pork belly stir-fried in miso oyster sauce
Sweet miso stir-fried with Eggplant and green pepper

Cluster3
Recipe title
Miso stir-fried with pork & eggplant & green pepper
For lunch! Miso stir-fried with eggplant
Miso stir-fried with plenty of vegetables
Pork miso stir-fried with eggplant and green pepper
Miso stir-fries with pork, eggplant and green pepper

Cluster2
Recipe title
Delicious! Pork curry
In a pressure cooker! Pork curry
Very easy! Pork curry in 15 minutes
Pork beans curry
Pork ginger curry

Cluster3
Recipe title
Beat the summer heat! summer vegetables curry
Plenty of summer vegetables curry
Keema curry with summer vegetables
Summer vegetables curry!!
Plenty of summer vegetables! Tomato curry

Cluster2
Recipe title
Simmered pork belly, radish and burdock
Simmered pork belly and root vegetables
In a rice cooker! Simmered pork and burdock
Delicious! Simmered pork and radish
Easy! Simmered pork belly and root vegetables

Cluster3
Recipe title
Easy pressure cooker recipe! Simmered ground pork
Simmered radish and ground pork
Simmered Chinese cabbage and pork meat balls
Simmered radish and ground pork
Simmered burdock, tofu and ground pork

Cluster2
Recipe title
Warm creamy stew
Cream stew with plenty of autumn vegetables
Simple! Cream stew for adult
Mellow chicken cream stew
Cream stew in a pressure cooker

Cluster3
Recipe title
Chicken tomato stew
Rich taste! Easy warm tomato stew
Warm tomato stew
Tomato stew with plenty of autumn vegetables
Tomato stew

Cluster2
Recipe title
Avocado and cottage cheese salad
Easy, avocado and cottage cheese salad
Avocado and cottage cheese salad!
Avocado, tomato and cottage cheese salad
Japanese-style salad with avocado, tomato and cheese

Cluster3
Recipe title
Easy! Salmon and avocado salad
Beautiful skin salad with avocado and salmon
Health & Beauty! Salmon and avocado salad
Tomato, salmon and avocado salad
Refreshing salad with avocado and salmon

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