The document discusses how Netflix optimizes personalized artwork for its titles to enhance user engagement and viewing decisions across its 130 million global members. It details the use of algorithms, such as bandit algorithms, to analyze user preferences and choose effective imagery based on various factors like viewing history and location. The document also highlights the challenges of image attribution, reward assignment, and maintaining user interest through diverse and representative artwork.

1. Artwork
Personalization
at Netflix
Fernando Amat
RecSys, Oct 2018

2. Quickly help members discover content they’ll love

3. Global Members, Personalized Tastes
130 Million Members
~180 Countries

4. 98% Match
Spot the
Algorithms!

5. Artwork Optimization

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. Intuition for Personalized Assets
● Emphasize themes through different artwork according to some
context (user, viewing history, country, etc.)
Preferences in genre

8. Intuition for Personalized Assets
● Emphasize themes through different artwork according to some
context (user, viewing history, country, etc)
Preferences in cast members

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. 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. 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. 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. ● 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. Explore
show?
Choose
Epsilon Greedy Example
εprofile
1-εprofile
εshow
1-εshow Personalized Image
Image
At Random

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. Take Fraction Example: Luke Cage
Take Fraction = 1 / 3
Play
No play
User A
User B
User C

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. 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. 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. Research
Directions

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. Automatic image selection
● Generating new artwork is costly and time consuming
● Develop algorithm to predict asset quality from raw image

23. Long-term Reward: Road to RL
● Maximize long term reward: reinforcement learning
○ User long term joy rather than plays

24. Thank you.
Fernando Amat (famat@netflix.com)
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