Gousto - Irene Iriarte Carretero

1. De-cyphering
recipes with neo4j
Irene Iriarte Carretero
GraphTour London March 2018

2. Fueling family life
through good food
Simple model
•You pick from 25+ recipes each week
•Exactly proportioned ingredients
•No planning, no supermarkets and no food waste!
Leading proposition
•Most choice (25 is just the start)
•Most delivery options
•Best price!

4. To recommend recipes to our customers:
Collaborative
Filtering

5. To recommend recipes to our customers:
Collaborative
Filtering

6. To recommend recipes to our customers:
Collaborative
Filtering

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

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

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

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

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

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

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

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

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

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

17. 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?

18. 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

19. 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

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

21. 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

22. 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

23. Future
- Where do people click?
- What does this tell us about the user?
- AI recipe development

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