1. Exploration cycle
finding a better dining experience:
a framework of meal-plates
China Takahashia, Mitsunori Matsushitaa, Ryosuke Yamanishia
a Kansai University, Japan
27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
Athens, Greece, 06-08 September 2023

2. INTRODUCTION
• Dining is not just for nutrition
• One of the “experiential content” that enriches our daily lives
• The appeal of this dining experience is not only influenced by the
deliciousness of the meal, but also by the presentation of the meal
• e.g., the eccentricity of ingredients, cooking methods, serving and plates
• These balances are important to enhance the appeal of the dining experience
• We focused on the “plates”
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3. • Plates are not just containers for serving meal
• If you are only looking for the role of putting meals in, one deep and
large plate should be enough
• However, there are various plates in the world
• We use it in various situations and in various meals
The selection of plates is one of the important factors contributing
to the enhancement of the dining experience
BACKGROUND
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4. Wouldn't you like to eat the right meal every day?
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5. • The selection of plates changes depending on the meal ingredients and
cooking method
• It is necessary to select according to the meal
• The current situation is…
• The user recalls his/her own dining experience and selects a plate
imaginatively
• Unable to improve because new discoveries and knowledge cannot
be obtained
PROBLEM (1)
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6. • In many plates, there are multiple choices of meals that can be served
• It is necessary to select according to the preference and inspiration of
the person serving meals
• The current situation is…
• Users do not know their own preferences
We provide a system for supporting user's exploration
to find a suitable combination of meals and plates
PROBLEM (2)
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7. IDEA: MEALS–PLATES EXPLORATION CYCLE
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• This cycle is developed based on the conventional flow of plate selection
a plate
meals
A Part of plate selection
A Part of meal selection
Expand
Focused
Focused
Expand
a meal
plates

8. IDEA: MEALS–PLATES EXPLORATION CYCLE
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a plate
meals
A Part of plate selection
A Part of meal selection
Expand
Focused
Focused
Expand
a meal
plates
computational support

9. A Part of plate selection
Recommend by
computer
Expand
Selection by user
Focused
Selection by user
Focused
Recommend by
computer
Expand
a plate
meals
A Part of meal selection
plates
a meal
Expand phase
Current situation
• New discoveries and knowledge
cannot be obtained
Our support
• Computer recommends multiple
meals(plates) from a single
plate(meal)
IDEA: MEALS–PLATES EXPLORATION CYCLE
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10. A Part of plate selection
Recommend by
computer
Expand
Selection by user
Selection by user
Recommend by
computer
Expand
a plate
meals
A Part of meal selection
plates
a meal
Focused phase
Focused
Focused
Current situation
• Users do not know their own
preferences
Our support
• User selects a single plate(meal)
from multiple plates(meals) using
refine search
IDEA: MEALS–PLATES EXPLORATION CYCLE
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11. A Part of plate selection
Recommend by
computer
Expand
Selection by user
Selection by user
Recommend by
computer
Expand
a plate
meals
A Part of meal selection
plates
a meal
Focused phase
Focused
Focused
• Users do not know their own
preferences
• User selects a single plate(meal)
from multiple meals(plates) using
filtering search
Not to explore individual plates or meals,
but to explore the plates by linking information about
the appropriate meal to each plate
Point of this idea
IDEA: MEALS–PLATES EXPLORATION CYCLE
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12. THE GOAL OF THIS RESEARCH
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• The user cyclically learns and selects the plate that matches the
meal and the meal that matches the plate
• The goal is to allow the user organize their own preferences and
select plates in an exploratory manner while understanding the
compatibility of meals with plates by myself.
Changes in users

13. Expand phase
(Computer recommends multiple meals(plates) from a single plate(meal))
• We need to identify the conditions that a meal suitable for the plate
should satisfy
• (A): converting meal information and plate information to
machine-readable data
• (B): associating meal information and plate information
converted to machine-readable data in (A)
HOW THE IDEA WORKS
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14. • Data source
• Recipes from a cooking site (Cookpad[1])
• Define the meal elements involved in selecting a plate
• Ingredients
• Cooking behavior
(じゃがいも, にんじん, 玉ねぎ … 炒める, 茹でる, 切る)
(potato, carrot, onion … fry, boil, cut)
(1,1,1,1,0,・・・,1)
Represented by a binary vector
[1] Cookpad Inc., 2015. Cookpad dataset, the informatics research data repository, the national institute of informatics (dataset), https://doi.org/10.32130/idr.5.1.
(A-1) CONVERTING MEAL INFORMATION
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15. • Data source
• E-commerce websites (Rakuten Ichiba Shopping Site[2])
• Define the plate elements involved in selecting a meal
• Size（the long side, the short side and height）→ measured value
• Shape (e.g., circles, corners, and flowers）→ binary
• Material (e.g., Japanese ceramics, lacquerware, and glass) → binary
(A-2) CONVERTING PLATE INFORMATION
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[2] Rakuten Inc., 2014. Rakuten dataset, the informatics research data repository, the national institute of informatics, https://doi.org/10.32130/idr.2.0.

16. • How do you associate these two data?
• Associate by “meal name”, which is a common item of two data
• Cooking websites include recipe names and category names
• Product descriptions on E-commerce websites include descriptions of
examples of meals used
Pasta, curry, and Tenshinhan
(B) ASSOCIATING MEAL AND PLATE INFORMATION
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17. • However, cooking websites and E-commerce websites do not have uniform
granularity of meal names
• meal names appeared in cooking websites are too fine
• e.g., Vegetable curry, indian curry, chicken curry…
• meal names appeared in the e-commerce website are too coarse
• e.g., curry
• Understanding the names of meal described in product descriptions on EC sites
• Obtained 117 new meal names not included in the meal category names on the cooking
site using CRF(Conditional Random Field)
• Hierarchical organization of meal names on recipe and e-commerce sites
(B) ASSOCIATING MEAL AND PLATE INFORMATION
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18. • (A): converting meal information and plate information to machine-readable
data
• (B): associating meal information and plate information converted to machine-
readable data in (A)
• The following data are linked by the above (A) and (B)
• Ingredients
• Cooking behavior
meal information plate information
Meal name • Size 23
• Shape
• Material
LINKED RESULTS
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19. Category: Curry
• Ingredients
• How to cook
Category: Stew
• Ingredients
• How to cook
Meal A
Meal B
This plate is perfect for serving pasta
Plate B
• Size
• Material
This plate is perfect for serving curry !
• Size
• Material
Plate A
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• Shape
• Shape
EXAMPLE OF LINKED RESULTS

20. Category: Curry
• Ingredients
• How to cook
Category: Stew
• Ingredients
• How to cook
This plate is perfect for serving curry !
• Size
• Material
Meal A
Meal B
Plate A
This plate is perfect for serving pasta
Plate B
• Size
• Material
Corresponds plate information to meal information
by meal name (e.g., curry)
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• Shape
• Shape
EXAMPLE OF LINKED RESULTS

21. Category: Curry
• Ingredients
• How to cook
Category: Stew
• Ingredients
• How to cook
Meal A
Meal B
This plate is perfect for serving curry !
• Size
• Material
Plate A
Applicable locations in the cycle
If the ingredients or how to cook of MealA and MealB
are similar, it is possible to serve MealB on PlateA
even if the category is not curry
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• Shape

22. Category: Curry
• Ingredients
• How to cook
This plate is perfect for serving curry !
• Size
• Material
Meal A
Plate A
This plate is perfect for serving pasta
Plate B
• Size
• Material
Applicable locations in the cycle
If PlateA and PlateB are similar in size, shape and material,
it is possible to serve MealB on PlateA even if curry is not
mentioned in the product description
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• Shape
• Shape

23. Focused phase
(User selects a single plate(meal) from multiple meals(plates) using filtering
search)
• Meal-focused phase
• Narrow down using conventional recipe recommendation technology
• e.g., meal similarity [3], ingredients [4], and preferences [5]
• Plate-focused phase
• Narrow down using the plate appearance characteristics
• e.g., color, pattern, shape, and size
HOW THE IDEA WORKS
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[3] Wang, L., Li, Q., Li, N., Dong, G., Yang, Y., 2008. Substructure smilarity measurement in chinese recipes, in: Proc. 17th Int. Conf. on World Wide Web, pp. 979–988.
[4] Zhang, Q., Hu, R., Namee, B., Delany, S., 2008. Back to the future: Knowledge light case base cookery, in: Proc. 9th ECCBR, pp. 239––248
[5] Geleijnse, G.,Wang, L., Li, Q., 2010. Promoting tasty meals to support healthful eating, in:Wellness Informatics (WI)Workshop at CHI 2010.

24. • The selection of plates is one of the important factors contributing to
the enhancement of the dining experience
• we provide a system for supporting user's exploration to find a suitable
combination of meals and plates
Using Suggested Cycle,
• Users can organize their own preferences and select plates in an
exploratory manner while understanding the compatibility of meals
with plates
CONCLUSION
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