Artwork Personalization at Netflix

A Netflix homepage without artwork. This is how historically our recommendation algorithms viewed a page.
Artwork for Stranger Things that each receive over 5% of impressions from our personalization algorithm. Different images cover a breadth of themes in the show to go beyond what any single image portrays.

Challenges

Contextual bandits approach

Model training

Potential signals

Image Selection

Performance evaluation

Offline

Figure 1: Simple example of calculating a replay metric from logged data. For each member, a random image was assigned (top row). The system logged the impression and whether the profile played the title (green circle) or not (red circle). The replay metric for a new model is calculated by matching the profiles where the random assignment and the model assignment are the same (black square) and computing the take fraction over that subset.
Figure 2: Average image take fraction (the higher the better) for different algorithms based on replay from logged image explore data. The Random (green) policy selects one image at random. The simple Bandit algorithm (yellow) selects the image with highest take fraction. Contextual Bandit algorithms (blue and pink) use context to select different images for different members.
Figure 3: Example of contextual image selection based on the type of profile. Comedy refers to a profile that mostly watches comedy titles. Similarly, Romance watches mostly romantic titles. The contextual bandit selects the image of Robin Williams, a famous comedian, for comedy-inclined profiles while selecting an image of a kissing couple for profiles more inclined towards romance.

Online

Conclusion

References

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