1. Data Mining on Food Recipes Kyung-Joong Kim March 22 2013

2. Cooking Book

3. Nowadays …

4. Recipe Analysis

6. Idea Recipes on the Web Recipes Database Data Mining Knowledg e

7. www.recipesource.com

10. Inside of HTML

11. Semi-Automatic Approach (with human experts)

12. New Approach Semi-Structured Structured

13. Recipe Types

16. Missing Data (1)

17. Missing Data (2)

18. Measures Alternatives Representative teaspoons tsp ts t teaspoon tablespoons tbs tb T tablespoon pounds lbs lb pound cups c cup

19. Ingredients

20. Cuts of Beef

21. Deletion of Words Small, Baby, Short, Thin, … Ground, Chopped, Crushed, Dried, Soaked, Cooked, … Fresh, Hot, Cold, … Round, …

22. Experiments Korean (104 recipes) –64 mastercook –40 meal-master Converting Results (Semi-Structured  Structured) – 99 successful conversion – 5 failures

23. Why Failure? (2) No information (2 Cases)

24. Why Failure? (2) Unusual structure (3 Cases)

25. Ingredients Q) How many ingredients? A) 186 ingredients

26. Ingredients Per Recipe 8.356 ± 3.694 ingredients Max : 22 (Northern Cabbage Kimchi)

28. Spice A culinary term (not a botanical category) Plant products used in flavoring food and beverages

29. Spices onionpeppergarlic gingerchilipaprika mustardcinnamonparsley sesamescallionvinegar soy sauce

30. Spice Analysis # of Spices in Ingredient : 46 (24.7%) # of Spices in Recipe : 4.365 ± 2.311 Recipes with SpicesRecipes without Spices 93 (89%)11 (11%)

31. # of Recipes (Only Spices)

32. Meat vs. Vegetable Recipes Total Vegetable Recipes Meat Recipes # of Recipes10460 (57.7%)44 (42.3%) # of Ingredients8.356 ± 3.6947.083 ± 3.57610.090 ± 3.131 # of Spices4.365 ± 2.3113.433 ± 2.2355.636 ± 1.753

33. Conclusions Automatic Analysis of Semi-Structured Recipes on the Web Insights from Data-Driven Analysis for Food

34. Future Works Apply the Analysis to Other Countries (Comparative Study) Apply Data Mining Algorithms on the Data