6. 6
Goal
Help members find content to watch and enjoy
to maximize member satisfaction and retention
7. 7
Everything is a Recommendation
Rows
Ranking
Over 75% of what
people watch comes
from our
recommendations
Recommendations
are driven by
Machine Learning
9. 9
Personalized genres
Genres focused on user interest
Derived from tag combinations
Provide context and evidence
How are they generated?
Implicit: Based on recent
plays, ratings & other
interactions
Explicit: Taste preferences
Hybrid: combine the above
10. 10
Similarity
Find something similar to
something you’ve liked
Because you watched rows
Also
Video display page
In response to user actions
(search, list add, …)
28. 28
Building a page algorithmically
Approaches
Template: Non-personalized layout
Row-independent: Greedy rank rows by f(r | u, c)
Stage-wise: Pick next rows by f(r | u, c, p1:n)
Page-wise: Total page fitness f(p | u, c)
Obey constraints per device
Certain rows may be required
Examples: Continue watching and My List
29. 29
Row Features
Quality of items
Features of items
Quality of evidence
User-row interactions
Item/row metadata
Recency
Item-row affinity
Row length
Position on page
Context
Title
Diversity
Freshness
…
30. 30
Page-level Metrics
How do you measure the quality of
the homepage?
Ease of discovery
Diversity
Novelty
…
Challenges:
Position effects
Row-video generalization
2D versions of ranking quality
metrics
Example: Recall @ row-by-column
0 10 20 30
Recall Row