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Artwork Personalization at Netflix Fernando Amat RecSys2018

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For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.

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Artwork Personalization at Netflix Fernando Amat RecSys2018

  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!