The document outlines the architecture and processes behind the personalization algorithms used by Netflix for its recommender system. It details the integration of consumer data science, big data models, and machine learning approaches in enhancing user engagement and retention. Additionally, it emphasizes the importance of metrics and A/B testing in optimizing these recommendation algorithms over time.

1. Building
Industrial-­‐scale
Real-­‐world
Recommender
Systems
September
11,
2012
Xavier
Amatriain
Personaliza8on
Science
and
Engineering
-­‐
Ne?lix
@xamat

2. Outline
1. Anatomy of Netflix Personalization
2. Data & Models
3. Consumer (Data) Science
4. Architectures

3. Anatomy of
Netflix
Personalization
Everything is a Recommendation

4. Everything is personalized
Ranking
Note:
Recommendations
Rows
are per household,
not individual user
4

5. Top 10
Personalization awareness
All Dad Dad&Mom Daughter All All? Daughter Son Mom Mom
Diversity
5

6. Support for Recommendations
Social Support 6

7. Watch again & Continue Watching
7

8. Genres
8

9. Genre rows
§ Personalized genre rows focus on user interest
§ Also provide context and “evidence”
§ Important for member satisfaction – moving personalized rows to top on
devices increased retention
§ How are they generated?
§ Implicit: based on user’s recent plays, ratings, & other interactions
§ Explicit taste preferences
§ Hybrid:combine the above
§ Also take into account:
§ Freshness - has this been shown before?
§ Diversity– avoid repeating tags and genres, limit number of TV genres, etc.

10. Genres - personalization
10

11. Genres - personalization
11

12. Genres- explanations
12

13. Genres- explanations
13

14. Genres – user involvement
14

15. Genres – user involvement
15

16. Similars
§ Displayed in
many different
contexts
§ In response to
user actions/
context (search,
queue add…)
§ More like… rows

17. Anatomy of a Personalization - Recap
§ Everything is a recommendation: not only rating
prediction, but also ranking, row selection, similarity…
§ We strive to make it easy for the user, but…
§ We want the user to be aware and be involved in the
recommendation process
§ Deal with implicit/explicit and hybrid feedback
§ Add support/explanations for recommendations
§ Consider issues such as diversity or freshness
17

18. Data
&
Models

19. § Plays
Big Data § Behavior
§ Geo-Information
§ Time
§ Ratings
§ Searches
§ Impressions
§ Device info
§ Metadata
§ Social
§ …
19

20. Big Data § 25M+ subscribers
@Netflix § Ratings: 4M/day
§ Searches: 3M/day
§ Plays: 30M/day
§ 2B hours streamed in Q4
2011
§ 1B hours in June 2012
20

21. Models
§ Logistic/linear regression
§ Elastic nets
§ Matrix Factorization
§ Markov Chains
§ Clustering
§ LDA
§ Association Rules
§ Gradient Boosted Decision Trees
§ …
21

22. Rating Prediction
22

23. 2007 Progress Prize
§ KorBell team (AT&T) improved by 8.43%
§ Spent ~2,000 hours
§ Combined 107 prediction algorithms with linear
equation
§ Gave us the source code

24. 2007 Progress Prize
§ Top 2 algorithms
§ SVD - Prize RMSE: 0.8914
§ RBM - Prize RMSE: 0.8990
§ Linear blend Prize RMSE: 0.88
§ Limitations
§ Designed for 100M ratings, we have 5B ratings
§ Not adaptable as users add ratings
§ Performance issues
§ Currently in use as part of Netflix’ rating prediction component

25. SVD
X[n x m] = U[n x r] S [ r x r] (V[m x r])T
§ X: m x n matrix (e.g., m users, n videos)
§ U: m x r matrix (m users, r concepts)
§ S: r x r diagonal matrix (strength of each ‘concept’) (r: rank of the matrix)
§ V: r x n matrix (n videos, r concepts)

26. Simon Funk’s SVD
§ One of the most
interesting findings
during the Netflix
Prize came out of a
blog post
§ Incremental, iterative,
and approximate way
to compute the SVD
using gradient
descent
http://sifter.org/~simon/journal/20061211.html 26

27. SVD for Rating Prediction
§ Associate each user with a user-factors vector pu ∈ ℜ f
§ Associate each item with an item-factors vector qv ∈ ℜ f
§ Define a baseline estimate buv = µ + bu + bv to account for
user and item deviation from the average
§ Predict rating using the rule
' T
r = buv + p qv
uv u
27

28. SVD++
§ Koren et. al proposed an asymmetric variation that includes
implicit feedback:
$ −
1
−
1 '
' T
& R(u) 2
r = buv + q &
uv v ∑ (ruj − buj )x j + N(u) 2
∑ yj ) )
% j∈R(u) j∈N (u) (
§ Where
§ qv , xv , yv ∈ ℜ f are three item factor vectors
§ Users are not parametrized, but rather represented by:
§ R(u): items rated by user u
§ N(u): items for which the user has given an implicit preference (e.g. rated
vs. not rated)
28

29. RBM

30. First generation neural networks (~60s)
Like Hate
§ Perceptrons (~1960) output units -
§ Single layer of hand-coded class labels
features
§ Linear activation function
§ Fundamentally limited in what non-adaptive
they can learn to do. hand-coded
features
input units -
features

31. Second generation neural networks (~80s)
Compare output to
Back-propagate correct answer to
compute error signal
error signal to
get derivatives
outputs
for learning
Non-linear
activation
function
hidden layers
input features

32. Belief Networks (~90s)
§ Directed acyclic graph stochas8c
composed of stochastic hidden
variables with weighted cause
connections.
§ Can observe some of the
variables
§ Solve two problems:
§ Inference: Infer the states of the
unobserved variables. visible
§ Learning: Adjust the effect
interactions between variables
to make the network more likely
to generate the observed data.

33. Restricted Boltzmann Machine
§ Restrict the connectivity to make learning easier.
§ Only one layer of hidden units.
§ Although multiple layers are possible hidden
§ No connections between hidden units. j
§ Hidden units are independent given the visible
states..
§ So we can quickly get an unbiased sample from
the posterior distribution over hidden “causes” i
when given a data-vector
visible
§ RBMs can be stacked to form Deep Belief
Nets (DBN)

34. RBM for the Netflix Prize
34

35. What about the final prize ensembles?
§ Our offline studies showed they were too
computationally intensive to scale
§ Expected improvement not worth the
engineering effort
§ Plus, focus had already shifted to other
issues that had more impact than rating
prediction...
35

36. Ranking Key algorithm, sorts titles in most
contexts

37. Ranking
§ Ranking = Scoring + Sorting + Filtering § Factors
bags of movies for presentation to a user § Accuracy
§ Goal: Find the best possible ordering of a § Novelty
set of videos for a user within a specific § Diversity
context in real-time § Freshness
§ Objective: maximize consumption § Scalability
§ Aspirations: Played & “enjoyed” titles have § …
best score
§ Akin to CTR forecast for ads/search results

38. Ranking
§ Popularity is the obvious baseline
§ Ratings prediction is a clear secondary data
input that allows for personalization
§ We have added many other features (and tried
many more that have not proved useful)
§ What about the weights?
§ Based on A/B testing
§ Machine-learned

39. Example: Two features, linear model
1
Predicted Rating
2
Final
Ranking
3
4
Linear
Model:
frank(u,v)
=
w1
p(v)
+
w2
r(u,v)
+
b
5
Popularity
39

40. Results
40

41. Learning to rank
§ Machine learning problem: goal is to construct ranking
model from training data
§ Training data can have partial order or binary judgments
(relevant/not relevant).
§ Resulting order of the items typically induced from a
numerical score
§ Learning to rank is a key element for personalization
§ You can treat the problem as a standard supervised
classification problem
41

42. Learning to Rank Approaches
1. Pointwise
§ Ranking function minimizes loss function defined on individual
relevance judgment
§ Ranking score based on regression or classification
§ Ordinal regression, Logistic regression, SVM, GBDT, …
2. Pairwise
§ Loss function is defined on pair-wise preferences
§ Goal: minimize number of inversions in ranking
§ Ranking problem is then transformed into the binary classification
problem
§ RankSVM, RankBoost, RankNet, FRank…

43. Learning to rank - metrics
§ Quality of ranking measured using metrics as
§ Normalized Discounted Cumulative Gain
n
DCG relevancei
NDCG = where DCG = relevance1 + ∑ and IDCG = ideal ranking
IDCG 2 log 2 i
§ Mean Reciprocal Rank (MRR)
1 1
MRR =
H
∑ rank(h ) where hi are the positive “hits” from the user
h∈H i
§ Mean average Precision (MAP)
N
∑ AveP(n) tp
MAP = n=1
where N can be number of users, items… and P =
N tp + fp
43

44. Learning to rank - metrics
§ Quality of ranking measured using metrics as
§ Fraction of Concordant Pairs (FCP)
§ Given items xi and xj, user preference P and a ranking method R, a
concordant pair (CP) is { xi , x j } s.t.P(xi ) > P(x j ) ⇔ R(xi ) < R(x j )
∑CP(x , x )
i j
§ Then FCP = i≠ j
n(n −1)
§ Others… 2
§ But, it is hard to optimize machine-learned models directly on
these measures
§ They are not differentiable
§ Recent research on models that directly optimize ranking
measures
44

45. Learning to Rank Approaches
3. Listwise
a. Directly optimizing IR measures (difficult since they are not differentiable)
§ Directly optimize IR measures through Genetic Programming
§ Directly optimize measures with Simulated Annealing
§ Gradient descent on smoothed version of objective function
§ SVM-MAP relaxes the MAP metric by adding it to the SVM constraints
§ AdaRank uses boosting to optimize NDCG
b. Indirect Loss Function
§ RankCosine uses similarity between the ranking list and the ground truth as
loss function
§ ListNet uses KL-divergence as loss function by defining a probability
distribution
§ Problem: optimization in the listwise loss function does not necessarily optimize
IR metrics

46. Similars
§ Different similarities computed
from different sources: metadata,
ratings, viewing data…
§ Similarities can be treated as
data/features
§ Machine Learned models
improve our concept of “similarity”
46

47. Data & Models - Recap
§ All sorts of feedback from the user can help generate better
recommendations
§ Need to design systems that capture and take advantage of
all this data
§ The right model is as important as the right data
§ It is important to come up with new theoretical models, but
also need to think about application to a domain, and practical
issues
§ Rating prediction models are only part of the solution to
recommendation (think about ranking, similarity…)
47

48. Consumer
(Data) Science

49. Consumer Science
§ Main goal is to effectively innovate for customers
§ Innovation goals
§ “If you want to increase your success rate, double
your failure rate.” – Thomas Watson, Sr., founder of
IBM
§ The only real failure is the failure to innovate
§ Fail cheaply
§ Know why you failed/succeeded
49

50. Consumer (Data) Science
1. Start with a hypothesis:
§ Algorithm/feature/design X will increase member engagement
with our service, and ultimately member retention
2. Design a test
§ Develop a solution or prototype
§ Think about dependent & independent variables, control,
significance…
3. Execute the test
4. Let data speak for itself
50

51. Offline/Online testing process
days Weeks to months
Offline Online A/B Rollout
Feature to
testing [success]
testing [success] all users
[fail]
51

52. Offline testing process
Initial
Hypothesis
Decide
Reformulate Model Rollout
Hypothesis Prototype Rollout
Train Model Feature to
[no] Wait for
all users
Try
offline
Online A/B
Results
Analyze
different
model?
[yes]
Test
testing
Results
[success]
[no] Hypothesis Significant
validated improvement
offline? on users?
[yes]
[fail] 52
[no]

53. Offline testing
§ Optimize algorithms offline
§ Measure model performance, using metrics such as:
§ Mean Reciprocal Rank, Normalized Discounted Cumulative Gain, Fraction of
Concordant Pairs, Precision/Recall & F-measures, AUC, RMSE, Diversity…
§ Offline performance used as an indication to make informed
decisions on follow-up A/B tests
§ A critical (and unsolved) issue is how offline metrics can
correlate with A/B test results.
§ Extremely important to define a coherent offline evaluation
framework (e.g. How to create training/testing datasets is not
trivial)
53

54. Online A/B testing process
Choose
Design A/
Control
B Test
Group
Decide
Reformulate Model Rollout
Hypothesis Prototype Rollout
Train Model Feature to
[no] Wait for
offline all users
Try
Offline Results
Analyze
different
model? testing
[yes]
Test
Results
Significant
Hypothesis [success] improvement
validated on users?
[no] offline? [yes]
[no] 54

55. Executing A/B tests
§ Many different metrics, but ultimately trust user
engagement (e.g. hours of play and customer retention)
§ Think about significance and hypothesis testing
§ Our tests usually have thousands of members and 2-20 cells
§ A/B Tests allow you to try radical ideas or test many
approaches at the same time.
§ We typically have hundreds of customer A/B tests running
§ Decisions on the product always data-driven
55

56. What to measure
§ OEC: Overall Evaluation Criteria
§ In an AB test framework, the measure of success is key
§ Short-term metrics do not always align with long term
goals
§ E.g. CTR: generating more clicks might mean that our
recommendations are actually worse
§ Use long term metrics such as LTV (Life time value)
whenever possible
§ In Netflix, we use member retention
56

57. What to measure
§ Short-term metrics can sometimes be informative, and
may allow for faster decision-taking
§ At Netflix we use many such as hours streamed by users or
%hours from a given algorithm
§ But, be aware of several caveats of using early decision
mechanisms
Initial effects appear to trend.
See “Trustworthy Online
Controlled Experiments: Five
Puzzling Outcomes
Explained” [Kohavi et. Al. KDD
12]
57

58. Consumer Data Science - Recap
§ Consumer Data Science aims to innovate for the
customer by running experiments and letting data speak
§ This is mainly done through online AB Testing
§ However, we can speed up innovation by experimenting
offline
§ But, both for online and offline experimentation, it is
important to choose the right metric and experimental
framework
58

59. Architectures
59

60. Technology
hQp://techblog.ne?lix.com
60

61. 61

62. Event & Data
Distribution
62

63. Event & Data Distribution
• UI devices should broadcast many
different kinds of user events
• Clicks
• Presentations
• Browsing events
• …
• Events vs. data
• Some events only need to be
propagated and trigger an action
(low latency, low information per
event)
• Others need to be processed and
“turned into” data (higher latency,
higher information quality).
• And… there are many in between
• Real-time event flow managed
through internal tool (Manhattan)
• Data flow mostly managed through
Hadoop.
63

64. Offline Jobs
64

65. Offline Jobs
• Two kinds of offline jobs
• Model training
• Batch offline computation of
recommendations/
intermediate results
• Offline queries either in Hive or
PIG
• Need a publishing mechanism
that solves several issues
• Notify readers when result of
query is ready
• Support different repositories
(s3, cassandra…)
• Handle errors, monitoring…
• We do this through Hermes
65

66. Computation
66

67. Computation
• Two ways of computing personalized
results
• Batch/offline
• Online
• Each approach has pros/cons
• Offline
+ Allows more complex computations
+ Can use more data
- Cannot react to quick changes
- May result in staleness
• Online
+ Can respond quickly to events
+ Can use most recent data
- May fail because of SLA
- Cannot deal with “complex”
computations
• It’s not an either/or decision
• Both approaches can be combined
67

68. Signals & Models
68

69. Signals & Models
• Both offline and online algorithms are
based on three different inputs:
• Models: previously trained from
existing data
• (Offline) Data: previously
processed and stored information
• Signals: fresh data obtained from
live services
• User-related data
• Context data (session, date,
time…)
69

70. Results
70

71. Results
• Recommendations can be serviced
from:
• Previously computed lists
• Online algorithms
• A combination of both
• The decision on where to service the
recommendation from can respond to
many factors including context.
• Also, important to think about the
fallbacks (what if plan A fails)
• Previously computed lists/intermediate
results can be stored in a variety of
ways
• Cache
• Cassandra
• Relational DB
71

72. Alerts and Monitoring
§ A non-trivial concern in large-scale recommender
systems
§ Monitoring: continuously observe quality of system
§ Alert: fast notification if quality of system goes below a
certain pre-defined threshold
§ Questions:
§ What do we need to monitor?
§ How do we know something is “bad enough” to alert
72

73. What to monitor
Did something go
§ Staleness wrong here?
§ Monitor time since last data update
73

74. What to monitor
§ Algorithmic quality
§ Monitor different metrics by comparing what users do and what
your algorithm predicted they would do
74

75. What to monitor
§ Algorithmic quality
§ Monitor different metrics by comparing what users do and what
your algorithm predicted they would do
Did something go
wrong here?
75

76. What to monitor
§ Algorithmic source for users
§ Monitor how users interact with different algorithms
Algorithm X
Did something go
wrong here?
New version
76

77. When to alert
§ Alerting thresholds are hard to tune
§ Avoid unnecessary alerts (the “learn-to-ignore problem”)
§ Avoid important issues being noticed before the alert happens
§ Rules of thumb
§ Alert on anything that will impact user experience significantly
§ Alert on issues that are actionable
§ If a noticeable event happens without an alert… add a new alert
for next time
77

78. Conclusions
78

79. The Personalization Problem
§ The Netflix Prize simplified the recommendation problem
to predicting ratings
§ But…
§ User ratings are only one of the many data inputs we have
§ Rating predictions are only part of our solution
§ Other algorithms such as ranking or similarity are very important
§ We can reformulate the recommendation problem
§ Function to optimize: probability a user chooses something and
enjoys it enough to come back to the service
79

80. More to Recsys than Algorithms
§ Not only is there more to algorithms than rating
prediction
§ There is more to Recsys than algorithms
§ User Interface & Feedback
§ Data
§ AB Testing
§ Systems & Architectures
80

81. More data +
Better models +
More accurate metrics +
Better approaches & architectures
Lots of room for improvement!
81

82. We’re hiring!
Xavier Amatriain (@xamat)
xamatriain@netflix.com