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Decyphering Recipes: Mapping ontologies for personalization

Speaker: Irene Iriarte Carretero

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Decyphering Recipes: Mapping ontologies for personalization

  1. 1. De-cyphering recipes with neo4j Irene Iriarte Carretero GraphConnect NY 2017
  2. 2. Introduction to Gousto
  3. 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. 4. Data Science Team Dejan Marc Manuel Cat Irene
  5. 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. 6. MySQL Data Warehouse Excel reports Google Analytics Airflow Snowplow
  7. 7. Marketing Attribution Stock Manipulation Forecasting Warehouse Optimisation
  8. 8. Personalisation
  9. 9. Personalisation
  10. 10. What does personalisation look like? “For you”
  11. 11. What does personalisation look like?
  12. 12. 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
  13. 13. To recommend recipes to our customers: Collaborative Filtering
  14. 14. To recommend recipes to our customers: Collaborative Filtering
  15. 15. To recommend recipes to our customers: Collaborative Filtering
  16. 16. To recommend recipes to our customers: Collaborative Filtering Pros: - Several data points for each recommendation Cons: - Cold Start - Sparsity
  17. 17. To recommend recipes to our customers: Content- based Filtering
  18. 18. To recommend recipes to our customers: Content- based Filtering
  19. 19. To recommend recipes to our customers: Content- based Filtering Pros: - Recipe cold start is not a problem Cons: - No information sharing across users - Serendipity
  20. 20. To recommend recipes to our customers: Collaborative Filtering Content- based Filtering+
  21. 21. To recommend recipes to our customers: Hybrid
  22. 22. To recommend recipes to our customers: Hybrid Hybrid model recommender developed by Maciej Kula (Lyst) https://arxiv.org/pdf/1507.08439.pdf
  23. 23. How to overcome cold start problem For new users: recipe battles vs
  24. 24. How to overcome cold start problem For new recipes: similarity from recipe properties -Italian -Pasta based -Non-vegetarian
  25. 25. Challenge
  26. 26. Brazilian Black Beans and Limey Chicken with Rice Cambodian Chicken Samla Curry with Rice
  27. 27. Brazilian Black Beans and Limey Chicken with Rice Cambodian Chicken Samla Curry with Rice ADVENTURE
  28. 28. Brazilian Black Beans and Limey Chicken with Rice Cambodian Chicken Samla Curry with Rice WHOLESOME
  29. 29. Beany Tacos with Sweetcorn and Chorizo and Sweet Potato Fries Pork, Pineapple and Red Onion Tacos
  30. 30. Beany Tacos with Sweetcorn and Chorizo and Sweet Potato Fries Pork, Pineapple and Red Onion Tacos KID-FRIENDLY
  31. 31. Beany Tacos with Sweetcorn and Chorizo and Sweet Potato Fries Pork, Pineapple and Red Onion Tacos CONVENIENCE
  32. 32. 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?
  33. 33. Solution
  34. 34. 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
  35. 35. 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
  36. 36. Why neo4j? - It allowed for flexibility in terms of describing recipe and ingredients attributes - We can easily create inferences from data attributes and relations
  37. 37. 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
  38. 38. 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
  39. 39. Future
  40. 40. Future - We could take personalisation one step further with Snowplow data - Where do people click? - What does this tell us about the user?
  41. 41. Future - Ontology could help us when substituting problematic ingredients for dietary requirements - AI recipe development
  42. 42. Thank you for listening! @GoustoTech techbrunch.gousto.co.uk

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