This document discusses building personalized data products and recommender systems using implicit and explicit user data. It describes how recommender systems work by using matrix factorization to learn latent factors about users and items from interaction data in order to predict ratings and rankings to drive personalized recommendations. The document also notes that recommender systems are commonly used by Netflix, Spotify, LinkedIn and Facebook to power personalized experiences and that even small improvements in recommendation quality can lead to significant business value.

1. Building Personalized
Data Products with Dato
Trey Causey
trey@dato.com

2. Questions?
• Now: We are monitoring chat window
• Later: Email me at trey@dato.com
• dato.com

3. What are data products?
• Products that produce and consume data.
• Products that improve as they produce and
consume data.
• Products that use data to provide a personalized
experience.
• Personalized experiences increase engagement
and retention.

4. What data?
• You probably already have this data
• Usage logs, transaction data, etc.
• Need a way to turn this existing data into
an intelligent application

5. Recommender systems
• Personalized experiences through
recommendations
• Recommend products, social network
connections, events, songs, and more
• Implicitly and explicitly drive many of
experiences you’re familiar with

6. Recommender uses
• Netflix, Spotify, LinkedIn, Facebook with the most
visible examples
• “You May Also Like”
“People You May Know”
“People to Follow”
• Also silently power many other experiences
• Product listings, up-sell options, add-ons,
• Netflix —> $1MM for 10% better

7. What data do you need?
• Required for implicit data
• User identifier
• Product identifier
• That’s it!
• Further customization
• Ratings (explicit data), counts
• Side data

8. Implicit data
• User x product
interactions
• Consumed / used /
clicked / etc.

9. How do recommenders work?
• Most basic: item similarity

10. Matrix factorization
• Treat users and products as a giant matrix
with (very) many missing values
• Users have latent factors that describe
how much they like various genres
• Items have latent factors that describe
how much like each genre they are

11. Matrix factorization
• Turn this into a fill-in-the-missing-value
exercise by learning the latent factors
• Implicit or explicit data
• Part of the winning formula for the Netflix
Prize
• Predict ratings or rankings

12. Matrix factorization

17. Fill in the blanks
• Learn the latent factors that minimize
prediction error on the observed values
• Fill in the missing values
• Sort the list by predicted rating &
recommend the unseen items

18. Rankings?
• Often less concerned with predicting
precise scores
• Just want to get the first few items right
• Screen real estate is precious
• Ranking factorization recommender

19. Side features
• Include information about users
• Geographic, demographic, time of day,
etc.
• Include information about products
• Product subtypes, geographic
availability, etc.
• Help with the cold start problem

20. How to choose which model?
• Select the appropriate model for your data
(implicit/explicit), if you want side features
or not, select hyperparameters, tune
them…
• … or let GraphLab Create do it for you and
automatically tune hyperparameters

21. Evaluation
• Train on a portion of your data
• Test on a held-out portion
• Ratings: RMSE
• Ranking: Precision, recall
• Business metrics
• Evaluate against popularity

22. Live demo
• Building and deploying a recommender
system with GraphLab Create and Dato
Predictive Services

23. Thank you!
• dato.com
• @datoinc
• trey@dato.com