Thai food are variety. How do Foreigners know Thai food with their preference.

1. THAI FOOD
RECOMMENDATION
Based on your preference!!
1. Russamee Nakaphun
2. Yada Limsuwan
3. Vanathip Gijruenthong
4. Chanokporn Youngpakool
5. Korrapin Pimapansri
Business Analytics and Data Science ,NIDA

2. Member
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1. น.ส. รัศมี นาคะพันธุ์ 6210422053​
2. น.ส. ญาดา ลิ้มสุวรรณ 6210422056
3. น.ส. วนาทิพย์ กิจเรือนทอง 6210422059
4. น.ส. ชนกพร ยังพะกูล 6210422065
5. น.ส. กรพินธุ์ พิมาพันธุ์ศรี 6220422056

3. WHAT?
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“Thai Food Recommendation for foreigners
based on their preference.”

4. WHY?
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• Thai food are variety.
• Foreigners knew only popular Thai food.
• Increase Thai food fans.
• More favorite Thai food.

5. What we do?
Step 1 Step 2 Step 3 Step 4 Step 5
Data Collection
- Recipes
(Yummly)
- Ingredients
- Food Categories
Data
Preparation
Data cleaning
Modeling
- Feature Selection
- Similarity model
Exploratory
Analysis
- Cuisines
- Ingredients
- Community
Detection
Evaluation
- Testing
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6. Data Collection (Recipes)
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- From 60+ recipe sites/blogs
- 27,638 Recipes (JSON Files)
Features:
Ingredients, Cuisine, Recipe Title,
Flavor, Ratings, Nutrition etc.

7. Data Collection (Ingredients)
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BBC Food
- 26 Webpages (A-Z) of Ingredient
Names
- 1000+ Individual Food Ingredients

8. Data Collection (Ingredients)
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Kaggle
- 2 JSON files with id and ingredients
FooDB
- Provides dataset that contains
information about compounds,
proteins, contents, nutrients , etc.
- We use only Food.csv
Features:
Name, Food group, Food subgroup, etc.

9. Data Preparation
9
28K JSON
Files
Remove recipes
- No flavor
information
- Duplicated by ID
Remove specific words
from ingredients
Scraped
Ingredient list
Import
Ingredient list
Combine, add plural
words and remove
specific words such
as low fat, fat free,
non fat, etc.
Clean
ingredients
Import Ingredient Category
Add ingredient
category to
recipes

10. Data Preparation
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Data Cleaning (Ingredients)
Clean
ingredients
Create Nested Dictionary
{Recipe:{Ingr:w}}
- Unweighted : 1
- Weighted : x gram per
1 serving
Convert Dictionary to
Dataframe.
(Dataframe is matrix
between recipes and
ingredients)

11. Exploratory Analysis
22,710
Recipes
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24
Countries
4,515
Ingredient
(25 Categories)

12. Exploratory Analysis
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Most commonly used ingredients.Top country cuisines

13. 13
Exploratory Analysis
Most commonly used ingredients by cuisines.

14. 14
Exploratory Analysis
Most commonly used ingredients by cuisines.

15. Exploratory Analysis
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Relation of ingredient between cuisines

16. Modeling
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Feature Selection
Ingredients
Flavor [0,1]
- Sweet
- Sour
- Bitter
- Piquant
- Salty
- MeatyProportion of Ingredients
( gram per serving)
Number of servings
Ingredient Category

17. Modeling
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Ingredient
(No weight)
Many Thai Coconut Curry Soup with Chicken !

18. Modeling
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Ingredient
(No weight)
FlavorIngredient
(Weight)
Category
SimJaccard
(Ingri , Ingrj)
SimCosine
(Ingri , Ingrj)
SimCosine
(Flavori, Flavorj)
SimJaccard
(Categoryi, Categoryj)
Recipe

19. Modeling
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SimJaccard
(Ingri , Ingrj)
SimCosine
(Ingri , Ingrj)
SimCosine
(Flavori, Flavorj)
SimJaccard
(Catagoriesi, Catagoriesj)
A x B x C x D x
+
All approach are combined together.
*A, B, C, D can be adjustable.
Similarity

20. Modeling
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Result

21. Modeling
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Problem of Limited memory
0
10
20
30
40
50
60
70
80
90
ComputationTime(S)
Computation Time (S) normal array/dataframe Computation Time (S) dask (chunk)
Model
Computation Time
(S)
Normal Chunk
case1 = ((jac_sim_ingredient)+(cos_sim_weighted_flavor)+(jac_sim_categories))/3 14.6 0.4
case2 = ((cos_sim_weighted_ingredient)+(cos_sim_weighted_flavor)+(jac_sim_categories))/3 78 4.69
case3 = (0.2*jac_sim_ingredient)+(0.5*cos_sim_weighted_flavor)+(0.3*jac_sim_categories) 15.4 0
case4 = (0.3*cos_sim_weighted_ingredient)+(0.5*cos_sim_weighted_flavor)+(0.2*jac_sim_categories) 15.9 0
case5 = (0.3*cos_sim_weighted_ingredient)+(0.2*cos_sim_weighted_flavor)+(0.5*jac_sim_categories) 15.7 0
case6 = (0.5*cos_sim_weighted_ingredient)+(0.2*cos_sim_weighted_flavor)+(0.3*jac_sim_categories) 15.8 1.56
case7 = (0.2*cos_sim_weighted_ingredient)+(0.3*cos_sim_weighted_flavor)+(0.5*jac_sim_categories) 16.1 1.56
case8 = (0.5*cos_sim_weighted_ingredient)+(0.3*cos_sim_weighted_flavor)+(0.2*jac_sim_categories) 15.7 0
case9 = (cos_sim_weighted_flavor+cos_sim_weighted_ingredient+jac_sim_categories+jac_sim_ingredient)/4 12.9 0.263
 Compute in “Chunk” very
faster than normal array!
 Fixed “Memory Error” problem

22. Evaluation
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Case Test Correct Accuracy (%)
1 100 41 41.00
2 97 40 41.24
3 96 41 42.71
4 112 44 39.29
5 111 46 41.44
6 111 46 41.44
Final Similarity = (0.2*jaccard(Ingredients))(0.5*cosine(flavors))+(0.3*jaccard(categories))
Process : Result (sample size = 628 times) :

23. Conclusion
● Community Detection
○ Thai food are in the clusters of Morocco, Mexican, Cuba, South West and India
○ Important common ingredients can be found in various countries for example Thai foods usually found fish sauce,
coconut milk, lemon juice, coriander, garlic
● Similarity Computation
○ Main problem is “Memory Error”, especially Jaccard Similarity
○ Necessary to apply “Parallel Computation” to solve this problem. This project used “Dask library” which the library
from “Chunk” concept. Moreover, it very fast running.
● Combination Models
○ This project selected 6 combination models from similarity matrix of Ingredients, favor, food categories
○ This 6 models differentiate from combination and weighting, as a result, every models have accuracy closely
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24. Future Works
● Should more Thai foods data with a variety
● More analysis with
○ Key Ingredients
○ Customer data for example rating
○ Methods for example puff, boil, fried, grill
● Test the system with more people, especially foreigners
● Develop the interface with image searching then recommend with text, image and restaurant location
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