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Artwork
Personalization
at Netflix
Fernando Amat
RecSys, Oct 2018
Quickly help members discover content they’ll love
Global Members, Personalized Tastes
130 Million Members
~180 Countries
98% Match
Spot the
Algorithms!
Artwork Optimization
Artwork Optimization
Goal: Recommend a personalized
artwork or imagery for a title to help
members decide if they will enj...
Intuition for Personalized Assets
● Emphasize themes through different artwork according to some
context (user, viewing hi...
Intuition for Personalized Assets
● Emphasize themes through different artwork according to some
context (user, viewing hi...
Bandit Algorithms Setting
For each (user, show) request:
● Actions: set of candidate images available
● Reward: how many m...
Numerous Variants
● Different Strategies: ε-Greedy, Thompson Sampling (TS), Upper Confidence
Bound (UCB), etc.
● Different...
Specific challenges
● Play attribution and reward assignment
○ Incremental effect of the image on top of recommender syste...
Specific challenges
● Change effect
○ Can changing images too often make users confused?
Session 1 Session 2 Session 3 ......
● We have control over the set of actions
○ How many images per show
○ Image design
● What makes a good asset?
○ Represent...
Explore
show?
Choose
Epsilon Greedy Example
εprofile
1-εprofile
εshow
1-εshow Personalized Image
Image
At Random
● Learn a binary classifier per image to predict probability of play
● Pick the winner (arg max)
Member
(context)
Features...
Take Fraction Example: Luke Cage
Take Fraction = 1 / 3
Play
No play
User A
User B
User C
● Unbiased offline evaluation from explore data
Offline metric: Replay [Li et al, 2010]
Offline Take Fraction = 2 / 3
User...
Offline Replay
● Context matters
● Artwork diversity matters
● Personalization wiggles
around most popular images
Lift in ...
Online results
● Rollout to our >130M member base
● Most beneficial for lesser known titles
● Compression from title -leve...
Research
Directions
Action selection orchestration
● Neighboring image selection influences result
● Title-level optimization is not enough
Ro...
Automatic image selection
● Generating new artwork is costly and time consuming
● Develop algorithm to predict asset quali...
Long-term Reward: Road to RL
● Maximize long term reward: reinforcement learning
○ User long term joy rather than plays
Thank you.
Fernando Amat (famat@netflix.com)
Blogpost
We are hiring!
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Artworks personalization on Netflix

Presentation given by Fernando Amat (Netflix) at the RecSys 2018 conference in Vancouver on artworks personalization.

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Artworks personalization on Netflix

  1. 1. Artwork Personalization at Netflix Fernando Amat RecSys, Oct 2018
  2. 2. Quickly help members discover content they’ll love
  3. 3. Global Members, Personalized Tastes 130 Million Members ~180 Countries
  4. 4. 98% Match Spot the Algorithms!
  5. 5. Artwork Optimization
  6. 6. Artwork Optimization Goal: Recommend a personalized artwork or imagery for a title to help members decide if they will enjoy the title or not.
  7. 7. Intuition for Personalized Assets ● Emphasize themes through different artwork according to some context (user, viewing history, country, etc.) Preferences in genre
  8. 8. Intuition for Personalized Assets ● Emphasize themes through different artwork according to some context (user, viewing history, country, etc) Preferences in cast members
  9. 9. Bandit Algorithms Setting For each (user, show) request: ● Actions: set of candidate images available ● Reward: how many minutes did the user play from that impression ● Environment: Netflix homepage in user’s device ● Learner: its goal is to maximize the cumulative reward after N requests Learner Environment Action Reward Context
  10. 10. Numerous Variants ● Different Strategies: ε-Greedy, Thompson Sampling (TS), Upper Confidence Bound (UCB), etc. ● Different Environments: ○ Stochastic and stationary: Reward is generated i.i.d. from a distribution specific to the action. No payoff drift. ○ Adversarial: No assumptions on how rewards are generated. ● Different objectives: Cumulative regret, tracking the best expert ● Continuous or discrete set of actions, finite vs infinite ● Extensions: Varying set of arms, Contextual Bandits, etc.
  11. 11. Specific challenges ● Play attribution and reward assignment ○ Incremental effect of the image on top of recommender system ● Only one image per title can be presented ○ Although inherently it is a ranking problem Would you play because the movie is recommended or because of the artwork? Or both?
  12. 12. Specific challenges ● Change effect ○ Can changing images too often make users confused? Session 1 Session 2 Session 3 ... Session N Sequence A Sequence B
  13. 13. ● We have control over the set of actions ○ How many images per show ○ Image design ● What makes a good asset? ○ Representative (no clickbait) ○ Differential ○ Informative ○ Engaging Actions Personal (i.e. contextual)
  14. 14. Explore show? Choose Epsilon Greedy Example εprofile 1-εprofile εshow 1-εshow Personalized Image Image At Random
  15. 15. ● Learn a binary classifier per image to predict probability of play ● Pick the winner (arg max) Member (context) Features Image Pool Model 1 Winner arg max Model 2 Model 3 Model 4 Greedy Policy Example
  16. 16. Take Fraction Example: Luke Cage Take Fraction = 1 / 3 Play No play User A User B User C
  17. 17. ● Unbiased offline evaluation from explore data Offline metric: Replay [Li et al, 2010] Offline Take Fraction = 2 / 3 User 1 User 2 User 3 User 4 User 5 User 6 Random Assignment Play? Model Assignment
  18. 18. Offline Replay ● Context matters ● Artwork diversity matters ● Personalization wiggles around most popular images Lift in Replay in the various algorithms as compared to the Random baseline
  19. 19. Online results ● Rollout to our >130M member base ● Most beneficial for lesser known titles ● Compression from title -level offline metrics due to cannibalization between titles
  20. 20. Research Directions
  21. 21. Action selection orchestration ● Neighboring image selection influences result ● Title-level optimization is not enough Row A (diverse images) Row B (the microphone row) Stand-up comedy
  22. 22. Automatic image selection ● Generating new artwork is costly and time consuming ● Develop algorithm to predict asset quality from raw image
  23. 23. Long-term Reward: Road to RL ● Maximize long term reward: reinforcement learning ○ User long term joy rather than plays
  24. 24. Thank you. Fernando Amat (famat@netflix.com) Blogpost We are hiring!

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