demo

1. Yan Wang

2. Why

3. Architecture
Recipe Name
Personalized
Ingredient
Matched
Recipe
Ingredient
(amount,
unit, name)
MySQL
Nltk
Scikit-learn
Flask
Javascript

4. Inside
Ingredients
Tastes
Recipe Name

5. Inside
Ingredients
Recipe Name

x = x0 x1 ⋅⋅⋅ xk ⋅⋅⋅ xn−1 xn
#
$
%
&, xi ∈ 0[ ,+∞)

6. Inside
Tastes
Recipe Name

y = y0 y1 y2 y3 y4 y5
!
"
#
$,
yi ∈ {0,17,33,50,67,83,100}

7. Inside
Ingredients
Tastes
Recipe Name

y = y0 y1 y2 y3 y4 y5
!
"
#
$,
yi ∈ {0,17,33,50,67,83,100}

x = x0 x1 ⋅⋅⋅ xk ⋅⋅⋅ xn−1 xn
#
$
%
&, xi ∈ 0[ ,+∞)

8. System
Evalua6on

9. Insight

10. Insight

11. Insight

12. Yan Wang

13. How
to
Use
● Favorite recipe as the benchmark
taste profile
● Taste simulator

14. Why
Random
Forest
● 1 multi-label model vs. 6 single-label modes
○ Output is independent given an input
● Classification vs. regression
○ Penalty function of regression makes more sense
● Selection among different regression models
○ Random forest gives the highest performance

15. Insight

16. Why
Corn
Tor6llas

17. Future
Work
● More data
● Normalize ingredient in terms of unit and kind
● Reduce the effects of “fake” features