2017 2nd International Conference on Communications, Information Management and Network Security (CIMNS 2017)

ISBN: 978-1-60595-498-1

A Recipe Recommendation System Based on Regional Flavor Similarity Lin-rong GUO, Shi-zhong YUAN*, Xue-hui MAO and Yi-ning GU

School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

*Corresponding author

Keywords: Chinese regional cuisines, Flavor preference, Flavor similarity, Recommendation system.

Abstract. One of the factors that affect food choice is flavor preferences. Although the flavors of

cuisines vary from one region to another, there are flavor similarities among the cuisines from

geographically adjacent regions. This paper presents a recipe recommendation system to recommend

a set of dishes from the various Chinese regional cuisines for a certain flavor preference in terms of

flavor similarity. The flavor similarities among the cuisines are determined using our previously

developed algorithms. First, the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm

is used to calculate the ingredient preferences of the regional cuisines, on the basis of which, each

dish of the regional cuisines is given a score. Then, we use the cosine similarity to measure the flavor

similarities among the regional cuisines. Finally, the Tidal-Trust algorithm is employed to choose the

dishes with the most similar flavors and recommend them to the user. The results of the questionnaire

evaluation for the system show the recommendations from the system are reasonable and acceptable

from professional chefs’ point of view.

Introduction

In recent years, with the rise of "kitchen economics"[1], a variety of recipes recommendation

systems emerge in an endless stream. However, the dietary preferences of users are influenced by

many factors, such as heredity, geographical environment, cultural environment, the current health

needs, dietary balance and some other factors. From the perspective of current health needs, T Ueta,

M Iwakami, T Ito et al. [2] proposed a goal-oriented recipe recommendation system, with which users

without any professional nutrition knowledge can get the appropriate recipes by entering natural

language, such as "I want to relieve fatigue". Ting Y H, Zhao Q, Chen R C et al.[3][4] recommended

a proper recipe based on the recipe ontology according to the user's health conditions and their

preferences. From the angle of dietary balance, Yoko MINO, Ichiro Kobayashi[5] combined recipes

with the user's schedule according to daily activity of the user. Naoki Shino, Ryosuke Yamanishi et

al.[6] put the research point on the menu, they considered that sometimes the ingredients in the recipe

could not be deployed or purchased at that time, and they found that other ingredients could be used to

replace the missing ingredient. Yajima A, Kobayashi I [7] proposed a method to recommend "easy"

cooking recipes by analyzing the content of recipes and considering user's conditions. In addition,

Zemeng Feng, Lifang Wu et al.[8] thought that user’s acceptance of a recipe is consistent with his/her

preference for ingredients in the recipe. Nevertheless, they didn't have much explanation for why

people like or dislike certain ingredients. Actually, here, it involves the influence of all the factors

mentioned above on people's eating habits influence.

As the saying goes, “A side water and soil raises a side people”, the influence of the geographical

environment on people's eating habits is very heavy. However, many of the existing recommendation

systems are not considered from a geographical point of view. Based on this situation, we develop a

recipe recommendation system based on regional flavor similarity. By this system, users can choose

their own love of a regional cuisine (such as Shandong), then find the other regional cuisine (such as

northeast dishes), which has a higher flavor similarity with the selected cuisine.

To implement this system, we need to calculate the flavor similarity between regions. In a previous

paper[9], we have studied the basic idea and algorithm design of flavor similarity computation. Firstly,

we use TF-IDF algorithm to get ingredients of regional preference recipes, and calculate scores of all

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recipes in this region. Then, we calculate the similarity between regions by cosine similarity

algorithm. Finally, tidal-trust algorithm is used to calculate the scores of other regions recommended

to the region. The higher scoring topN recipes are recommended to users.

The system based on regional flavor similarity is different from [2][3][5][7][8]. [2][3][5][7][8]

consider the user's health status, daily activities, personal preferences respectively. They often require

direct user participation, but in fact, for various reasons, users sometimes fail to provide accurate

measurement indicators, which can often lead to inaccurate results. Therefore, we provide users with

recipes to meet their needs and their regional characteristics of the diet by regional flavor similarity.

Architectural Design and Functionality

Our system has two main functions: (1) Query of high frequency ingredients in recipes. Users can

find ingredients used frequently in a region and understand the region's taste preferences. (2)

Recommendation between regional flavor similar cuisines. Users can obtain recipes from other

regions, which has a higher degree of similarity with the selected region, according to the ranking to

satisfy his/her need.

Architectural Design

Our system is divided into 4 layers, as shown in Figure 1. (1) The first layer is data collection layer.

Since our system is based on Chinese mainland provinces and cities, we collect relevant data from

the Chinese mainland's largest food web site “Meishi Jie”. (2) The second layer is data processing

layer. After collecting the data, we use algorithms mentioned above to calculate regional flavor

similarity. (3) The third layer is data storage layer. We store the data of regional flavor similarity

calculated by algorithms in database. (4) The fourth layer is portal layer. Our system provides two

main functions: high frequency ingredients inquiry of various cuisines; recommendation of similar

cuisines between different regions.

Figure 1. Architectural design.

Structural Components and Functionality

Data Collection. The data of our system comes from “Meishi jie”, which is one of the largest

recipe information platforms in Chinese Mainland. We have obtained 20 Chinese cuisines on

"Meishi Jie" by data crawling, including 8498 recipes and 2911 ingredients.

Data Processing. After data collection, we divide the data into 2 data sets, one is a set of 8498

recipes involving 20 regions, and the other is a set of component containing 2911 ingredients.

a) Ingredient preferences and each recipe’s score of the regional cuisines. In order to calculate

the score of recipes, firstly we calculate the score of various ingredients in different regions by

TF-IDF algorithm[6].

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, , ,s t s t s tI TF IDF (1)

If “s” is token as Shandong cuisine and “t” is token as potatoes, we can calculate the degree of

Shandong people's preference for potatoes by formula (1). The weight of Shandong people's

preference for partial ingredient is shown in Table 1.

There is a recipe called "potato stewed egg" in Shangdong cuisine, including egg, salt, garlic,

onions, potatoes, black pepper. We added up the scores of these ingredients by formula (2), which

was the final score of this recipe.

,( )s s t

t R

Score R I

(2)

b) Regional flavor similarity calculation. For example, the Shandong people's evaluation of the

2911 ingredients is as follows: , ,1 ,2 ,2911[ , ,..., ]s k s s sI I I I , and the Jiangsu people’s evaluation of the

2911 ingredients is as follows: , ,1 ,2 ,2911[ , ,..., ]j k j j jI I I I . We can calculate the taste similarity

between Shandong and Jiangsu people by cosine similarity algorithm and formula (3), which is the

flavor similarity between Shandong cuisine and Jiangsu cuisine. The flavor similarity between

Shangdong cuisine and other cuisines is shown in Figure 2 below.

2911

, ,

1

2911 29112 2

, ,

1 1

cos ( , )

( ) ( )

s k j k

ks j

s k j k

k k

I I

ine I I

I I

(3) c) Similar cuisine recommended. For instance, we recommend recipes from Shandong cuisine to

Jiangsu cuisine by tidal-trust algorithm and formula (4).

,( ) cos ( , )s j s j sScore R ine I I R (4)

In the same way, we can also recommend recipes from other cuisines to Jiangsu cuisine and sort

all recipes, then choose the higher n recipes. These recipes combine the local characteristics and

satisfy the tastes of the people in Jiangsu.

Figure 2. The flavor similarity between Shangdong cuisine and other cuisines.

Data Storage. After handling related data sets through above algorithms, we design two tables:

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Table 1. Ingredient preference table.

Table 2. Ingredient Search Data Table.

Table 3. Recipe Similarity Score Data Table.

a) Ingredient search data table(Table 2). This table is used to keep the basic information about

the ingredients. To avoid confusion, "gid" field is requested to be unique in the table. And other

fields are not null values.

b) Recipe similarity score data table (Table 3). This table is used to store the scores, ingredients,

cooking method and other relevant information of recipes in other areas similar to the chosen

recipes. Similarly, "rid" field is requested to be unique in the table. And other fields are not null

values.

Portal. The system has two main functions as follows: high frequency ingredient inquiry of

various cuisines and recommendation of similar cuisines.

a) High frequency composition inquiry of various cuisines

For a particular cuisine, we can use a "high frequency ingredients" to search for a number of

recipes that are frequently used in the cuisine, and also to learn more about the taste preferences of

people in this area.

For example, the high frequency ingredients of Shandong cuisine are tofu, beef, cabbage etc.

Here, we represent it by histogram (Figure 3). Through the histogram, we can see that tofu is used

most frequently in Shandong cuisine, its frequency is 30%.

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Figure 3. High frequency ingredient.

b) Recommendation of similar cuisines between different regions

Other regional cuisines are eventually recommended to the cuisine we selected after scoring and

screening. For example, if you want to open a restaurant in Shandong, according to the score, you

can choose fresh milk grill, Susan fish, pouch fish, etc. of Shanxi cuisine, Braised tripe with red

beans of Hunan cuisine, lotus set crabs of Hubei cuisine and so on (Figure 4). You can take them as

the introduction of new recipes to attract customers.

Figure 4. Similar recipes recommendation.

Conclusions and Future Work

From the perspective of regional dietary preferences, our system implements recipe

recommendation based on regional flavor similarity. Through this system, users can specify a

cuisine according to their own preferences, and find other cuisines similar to the cuisine they have

specified to satisfy their taste bud requirements. And the restaurant operators can realize "win-win"

between the restaurant management and the customers' needs by our system.

In order to evaluate whether the system is reasonable and acceptable, we have invited

professional chefs to try and grade the system. Finally, the average score of Shandong was 7.98, the

average score of Jiangsu was 7.7 (the highest score was 10 points). This shows that this system is

convenient and practical in daily life, and it has good user experience and high anti loudness. Thus,

it has a certain value in use.

However, there are still some areas for improvement in the system. For example, the algorithms

we studied before did not study the quantity of each ingredient in the recipes. And the function of

the page needs to be optimized. It is better to add search function to the sub pages of each cuisine,

and search for other recipes with higher similarity to a specific ingredient in the cuisine. But

because the data and computation of this index are too complex, function optimization needs further

study.

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