Building Personalized Data Products with Dato
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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.
Building Personalized Data Products with Dato
- 1. Building Personalized Data Products with Dato Trey Causey [email protected]
- 2. Questions? ⢠Now: We are monitoring chat window ⢠Later: Email me at [email protected] ⢠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 youâre familiar with
- 6. Recommender uses ⢠Netflix, Spotify, LinkedIn, Facebook with the most visible examples ⢠âYou May Also Likeâ âPeople You May Knowâ âPeople to Followâ ⢠Also silently power many other experiences ⢠Product listings, up-sell options, add-ons, ⢠Netflix â> $1MM for 10% better
- 7. What data do you need? ⢠Required for implicit data ⢠User identifier ⢠Product identifier ⢠Thatâs 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
- 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 ⢠[email protected]