This document describes a recipe suggestion tool called Celebrity Chef Recipe Suggester. It uses data science techniques to match users' ingredients to recipes created by celebrity chefs. The tool scrapes recipe data from Food Network and Yummly, stores it in MongoDB, and uses k-means clustering and Word2Vec models with cosine similarity to find recipes that best match a user's ingredients. A Flask app allows users to input ingredients and get suggested recipes that include the chef, course, difficulty, and timing. The tool could be expanded to return a top number of results, allow custom recipe corpora, or suggest future recipes based on items in a user's fridge.

1. Celebrity Chef
Recipe
Suggester
Ben Ellentuck
Metis Data Science Bootcamp
March 31, 2016

2. Problem
Wouldn’t you
like to see what
celebrity chefs
would do with
your ingredients?

3. Fridge full of
food: what do
you do with it?
Problem

4. Inkling

5. Inkling
Alton Brown

6. Inkling
Alton Brown,
Celebrity Chef

7. Inkling
Wouldn’t you
like to see what
celebrity chefs
could do with your
ingredients?

8. IdeaInspiration
What if you got
to set up your
own “celebrity
Iron Chef”?

9. Inspiration
Mark Dacascos
Chairman, Iron Chef

10. Iron Chef, Chopped:
Given ingredients,
time constraints,
riff up courses!
Inspiration

11. Idea
What if you got
to set up your
own “celebrity
Iron Chef”?

12. Data Science
Does the Trick.

13. Data:
Food Network, Yummly
Data Science
Does the Trick.

14. Data:
Food Network, Yummly
Storage:
MongoDB
Data Science
Does the Trick.

15. Data:
Food Network, Yummly
Storage:
MongoDB
Model:
K-means
Data Science
Does the Trick.

16. Data:
Food Network, Yummly
Storage:
MongoDB
Model:
K-means
Metric:
Jaccard Similarity
Data Science
Does the Trick.

17. Data:
Food Network, Yummly
Storage:
MongoDB
Model:
Word2Vec
Data Science
Does the Trick.

18. Data:
Food Network, Yummly
Storage:
MongoDB
Model:
Word2Vec
Metric:
Cosine Similarity
Data Science
Does the Trick.

19. Data:
Food Network, Yummly
Storage:
MongoDB
Model:
Word2Vec
Metric:
Cosine Similarity
(contextually similar
ingredients blend better!)
Data Science
Does the Trick.

20. Flask App

21. Ingredients

22. Ingredients + Chefs

23. Ingredients + Chefs

24. Ingredients + Chefs + Course + Difficulty + Timing

25. Ingredients + Chefs + Course

26. Ingredients + Chefs + Course + Difficulty

27. Ingredients + Chefs + Course + Difficulty + Timing

28. Ingredients + Chefs + Course + Difficulty + Timing →

29. Ingredients + Chefs + Course + Difficulty + Timing → Results

30. Ingredients + Chefs + Course + Difficulty + Timing → Results

31. Ingredients + Chefs + Course + Difficulty + Timing → Results

32. Ingredients + Chefs + Course + Difficulty + Timing → Results

33. Ingredients + Chefs + Course + Difficulty + Timing → Results

34. Results Rank Recipes:

35. Results Rank Recipes:

36. Results Rank Recipes:

37. Results Rank Recipes:

38. Results Rank Recipes:

39. Further
Applications

40. 1. Top n Results
Further
Applications

41. 1. Top n Results
Further
Applications

42. 1. Top n Results
2. Custom Corpora
Further
Applications

43. 1. Top n Results
2. Custom Corpora
3. Future Fridges
Further
Applications

44. Thanks!
Ben Ellentuck
ellentuckben@gmail.com
linkedin.com/in/benellentuck
github.com/bellentuck