Speaker: Irene Iriarte Carretero

1. De-cyphering
recipes with neo4j
Irene Iriarte Carretero
GraphConnect NY 2017

2. Introduction
to Gousto

3. - UK-based online recipe box service
- Leaders in choice, offering 22 meals a
week
- Deliver exactly proportioned
ingredients with step-by-step recipe
cards in 2-3 days
- No planning, no supermarkets and no
food waste – you just cook and eat!

4. Data Science Team
Dejan Marc
Manuel Cat Irene

5. MySQL
Data Warehouse
Excel reports
Google
Analytics
- Started our data journey early on
- Many external data sources
- Lots of ad-hoc analyses
- Lack of being able to track
customer journeys

6. MySQL
Data Warehouse
Excel reports
Google
Analytics
Airflow
Snowplow

8. Marketing
Attribution
Stock
Manipulation
Forecasting
Warehouse
Optimisation

10. Personalisation

11. Personalisation

12. What does personalisation look like?
“For you”

13. What does personalisation look like?

14. What does personalisation look like?
- Default customers – on a subscription and
automatically get allocated recipes
- Better retention when they are happy with their
allocated choices
- Personalise their recipes

15. To recommend recipes to our customers:
Collaborative
Filtering

16. To recommend recipes to our customers:
Collaborative
Filtering

17. To recommend recipes to our customers:
Collaborative
Filtering

18. To recommend recipes to our customers:
Collaborative
Filtering
Pros:
- Several data points for each
recommendation
Cons:
- Cold Start
- Sparsity

19. To recommend recipes to our customers:
Content-
based
Filtering

20. To recommend recipes to our customers:
Content-
based
Filtering

21. To recommend recipes to our customers:
Content-
based
Filtering
Pros:
- Recipe cold start is not a
problem
Cons:
- No information sharing across
users
- Serendipity

22. To recommend recipes to our customers:
Collaborative
Filtering
Content-
based
Filtering+

23. To recommend recipes to our customers:
Hybrid

24. To recommend recipes to our customers:
Hybrid
Hybrid model recommender
developed by Maciej Kula (Lyst)
https://arxiv.org/pdf/1507.08439.pdf

25. How to overcome cold start problem
For new users: recipe battles
vs

26. How to overcome cold start problem
For new recipes: similarity from recipe properties
-Italian
-Pasta based
-Non-vegetarian

27. Challenge

28. Brazilian Black Beans
and Limey Chicken with
Rice
Cambodian Chicken
Samla Curry with Rice

29. Brazilian Black Beans
and Limey Chicken with
Rice
Cambodian Chicken
Samla Curry with Rice
ADVENTURE

30. Brazilian Black Beans
and Limey Chicken with
Rice
Cambodian Chicken
Samla Curry with Rice
WHOLESOME

31. Beany Tacos with
Sweetcorn and Chorizo
and Sweet Potato Fries
Pork, Pineapple and Red
Onion Tacos

32. Beany Tacos with
Sweetcorn and Chorizo
and Sweet Potato Fries
Pork, Pineapple and Red
Onion Tacos
KID-FRIENDLY

33. Beany Tacos with Sweetcorn
and Chorizo and Sweet
Potato Fries
Pork, Pineapple and Red
Onion Tacos
CONVENIENCE

34. Recipe Similarity
- Ingredients in common offer basic recipe similarity score
- Not good enough for our purposes
- We want to take into account subjective aspects:
• Cuisines
• Type of dishes
• Presentation
• Why is the customer using our service?

35. Solution

36. Ontology in neo4j
Ontology: is a formal naming and definition of the types,
properties, and interrelationships of the entities that
fundamentally exist for a particular domain

37. Why neo4j?
- Recipe & ingredient attributes are
highly interconnected
- In order to capture the different
point of views, it was vital that we
were able to easily explore
relations between the data

38. Why neo4j?
- It allowed for flexibility in terms of
describing recipe and ingredients
attributes
- We can easily create inferences
from data attributes and
relations

39. Calculating Similarities
- Supervised
Use tagged data to calculate weights of different attribute to
fit to training data
- Unsupervised
Use tagged data to validate our unsupervised model
- We will be using what customers are and are not ordering
as feedback

40. Benchmarking
- In order to benchmark our
similarity scores with those
coming from humans
- We set up a RecipeBot on Slack
that asked Gousto employees to
rate the similarity of certain
recipes
- Gathered thousands of answers

41. Future

42. Future
- We could take personalisation one step further with
Snowplow data
- Where do people click?
- What does this tell us about the user?

43. Future
- Ontology could help us when
substituting problematic
ingredients for dietary
requirements
- AI recipe development

44. Thank you for
listening!
@GoustoTech
techbrunch.gousto.co.uk