Building Incrementally Trained Global Deep Learning Recommender System
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This document discusses Netflix's global deep learning recommender system model. It describes how Netflix recommends content to over 150 million members across 190 countries using personalized recommendations. The system utilizes collaborative filtering techniques like soft clustering models to group users with similar tastes and generate weighted popularity votes. It also leverages topic models to model users' tastes as distributions over topics and content. The challenges of scaling these models globally to account for factors like country-specific catalogs and trends over time are discussed. The solution presented is to incrementally train the models by first censoring unavailable content and adding contextual variables, then periodically training warm start models with new embeddings and parameters to efficiently update the models at scale.
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Building Incrementally Trained Global Deep Learning Recommender System
- 1. Building an Incrementally Trained Global Deep Learning Recommender System Model Anoop Deoras, Ko-Jen (Mark) Hsiao adeoras@netflix.com MLConf, San Francisco 11/08/2019 @adeoras
- 3. ~150M Members, 190 Countries
- 4. ● Recommendation Systems are means to an end. ● Our primary goal: ○ Maximize Netflix member’s enjoyment of the selected show ■ Enjoyment integrated over time ○ Minimize the time it takes to find them ■ Interaction cost integrated over time Personalization ● Personalization
- 5. Everything is a recommendation!
- 6. Ordering of the titles in each row is personalized
- 7. Selection and placement of the row types is personalized
- 12. We Personalize our recommendation! This Talk Answers: HOW ?
- 13. Basic Intuition behind Collaborative Filtering ● Imagine you walked into a room full of movie enthusiasts, from all over the world, from all walks of life, and your goal was to come out with a great movie recommendation. ● Would you obtain popular vote ? Would that satisfy you ?
- 14. Basic Intuition behind Soft Clustering Models ● Now consider forming groups of people with similar taste based on the videos that they previously enjoyed.
- 18. Basic Intuition behind Soft Clustering Models ● Describe yourself using what you have watched. ● Try to associate yourself with these groups and obtain a weighted “personalized popularity vote”.
- 19. Distribution over the topics and over the videos 0.01 0.63 0.22 0.15
- 20. Topic Models (Latent Dirichlet Alloc) K U P α θ φt v β Total Topics Taste Convex Combinations of topics proportions and movie proportions within topic
- 21. OUR ALGORITHMS ARE GLOBAL AND THEY HELP LOCAL STORIES BE HEARD GLOBALLY
- 22. GLOBAL ALGORITHMS foster GLOBAL COMMUNITIES Thanks to Sudeep Das for contributing this beautiful slide.
- 23. Country Context in LDA models Users in Country A play both Friends and HIMYM Users in Country B cannot play both Friends and HIMYM Country A catalog Country B catalog Model is forced to split HIMYM plays. topic k Outcome: Parameters are being consumed to explain catalog differences. topic j Topic with high mass on Friends and HIMYM Topic with high mass on HIMYM Thanks to Ehtsham Elahi for contributing this slide.
- 24. Catalogue Censoring in Topic Models K U P α θ φt v β Total Topics Taste c Censoring pattern m Global Recommendation System for Overlapping Media Catalogue, Todd et.al., US Patent App
- 25. ALGORITHMS NEED TO CAPTURE THE TREND
- 26. Time context in Topic Models K U P α θ φt v β Total Topics Taste m Observed time µ Topics over Time: A Non Markov Continuous-Time Model fo Topic Trends. , Wang et.al., KDD 2006
- 27. Fully contextualizing Topic Models K U P α θ φt v β Total Topics Taste m Observed time µ c Censoring pattern m
- 28. SIMPLE !
- 30. Differentiation manual Time Consuming Poor Scaling Symbolic Time Efficient Excellent for scaling Gift of Deep Learning: Automatic Differentiation
- 31. Variational Autoencoders zu u Taste fθ 𝞵 𝞼 u Encoder Decoder fѰ fѰ DNN Soft-max over entire vocabulary Variational Autoencoders for Collaborative Filtering, Liang et al. WWW (2018)
- 32. ReLU ReLU User Representation Feed Forward Country Catalogue Country ● Create a censored mask with out of catalogue videos ● Mask the output layer (logits) ● Use the masked layer for cross entropy loss. How to do country contextual modeling ?
- 33. ReLU ReLU User Representation Country Feed Forward Country Catalogue Save Model Energy in Learning Catalogue Differences
- 34. ReLU ReLU User Representation Country Feed Forward Adding Time is Easy too Time at Serving
- 35. Time to train is large ! Catalogue changes quite fast DL Model P( | U, C, T) Cannot estimate as not in our model’s vocabulary
- 36. Incrementally Train the Models time Fully Trained Model Additional Nodes and Parameters
- 37. RECIPE 1. CENSOR 2. ADD CONTEXT VARIABLES TO THE MODEL 3. DO; EVERY FEW DAYS a. TRAIN A WARM START MODEL WITH (1 & 2) 4. DO; EVERY FEW HOURS a. TAKE THE MODEL FROM (3) b. ADD NEW EMBEDDINGS c. ADD NEW PARAMETERS d. FINE TUNE
- 38. THANK YOU ! Questions ? Anoop Deoras, Ko-Jen (Mark) Hsiao adeoras@netflix.com @adeoras Sincere thanks to a lot of my Netflix Colleagues: Aish Fenton, Dawen Liang and Ehstham Elahi for contributing to the ideas discussed here.