1. 11
Learning a Personalized Homepage
Justin Basilico
Page Algorithms Engineering April 11, 2014
NYC 2014
@JustinBasilico

2. 2

3. 3
Change of focus
2006 2014

4. 4
Netflix Scale
 > 44M members
 > 40 countries
 > 1000 device types
 > 5B hours in Q3 2013
 Plays: > 30M/day
 Log 100B events/day
 31.62% of peak US
downstream traffic

5. 55
Approach to Recommendation
“Emmy Winning”

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

8. 8
Top Picks
Personalization awareness
Diversity

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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, …)

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Support for Recommendations
Social Support

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Learning to Recommend

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Machine Learning Approach
Problem
Data
ModelAlgorithm
Metrics

14. 14
Data
 Plays
 Duration, bookmark, time, devic
e, …
 Ratings
 Metadata
 Tags, synopsis, cast, …
 Impressions
 Interactions
 Search, list add, scroll, …
 Social

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Models & Algorithms
 Regression (Linear, logistic, elastic net)
 SVD and other Matrix Factorizations
 Factorization Machines
 Restricted Boltzmann Machines
 Deep Neural Networks
 Markov Models and Graph Algorithms
 Clustering
 Latent Dirichlet Allocation
 Gradient Boosted Decision
Trees/Random Forests
 Gaussian Processes
 …

16. 16
Offline/Online testing process
Rollout
Feature to
all users
Offline
testing
Online A/B
testing[success] [success]
[fail]
days Weeks to months

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Rating Prediction
 First progress prize
 Top 2 algorithms
 Matrix Factorization (SVD++)
 Restricted Boltzmann Machines
(RBM)
 Ensemble: Linear blend
R
Videos
≈
Users
U
V
(99% Sparse) d
Videos
Users
d×

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Ranking by ratings
4.7 4.6 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5
Niche titles
High average ratings… by those who would watch it

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RMSE

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Learning to Rank
 Approaches:
 Point-wise: Loss over items
(Classification, ordinal regression, MF, …)
 Pair-wise: Loss over preferences
(RankSVM, RankNet, BPR, …)
 List-wise: (Smoothed) loss over ranking
(LambdaMART, DirectRank, GAPfm, …)
 Ranking quality measures:
 NDCG, MRR, ERR, MAP, FCP, Precision@N
, Recall@N, …
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100
Importance
Rank
NDCG MRR FCP

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Example: Two features, linear model
Popularity
PredictedRating
1
2
3
4
5
Linear Model:
frank(u,v) = w1 p(v) + w2 r(u,v)
FinalRanking

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Ranking

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Putting it together
“Learning to Row”

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Page-level algorithmic challenge
10,000s of
possible
rows …
10-40
rows
Variable number of
possible videos per
row (up to thousands)
1 personalized page
per device

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Balancing a Personalized Page
vs.Accurate Diverse
vs.Discovery Continuation
vs.Depth Coverage
vs.Freshness Stability
vs.Recommendations Tasks

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2D Navigational Modeling
More likely
to see
Less likely

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Row Lifecycle
Select
Candidates
Select
Evidence
Rank
Filter
Format
Choose

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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

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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
 …

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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

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Conclusions

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Evolution of Recommendation Approach
Rating Ranking Page Generation
4.7

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Research Directions
Context
awareness
Full-page
optimization
Presentation
effects
Social
recommendation
Personalized
learning to rank
Cold start

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Thank You Justin Basilico
jbasilico@netflix.com
@JustinBasilico
We’re hiring