These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.

1. Personalizing
“The Netflix Experience”
with Deep Learning
Anoop Deoras
AI NextCon, Seattle
01/23/2019
@adeoras

2. ● Personalization and Recommendations at Netflix
● Discuss evolution of latent models in the Recommender System space
● Showcase some experimental results and interesting findings
● Take away points
Theme of the talk

3. Netflix

4. ~139M Members, 190 Countries

5. ● Recommendation Systems are means to an end.
● Our primary goal:
○ Maximize Netflix member’s enjoyment of the selected show
■ Enjoyment integrated over time
○ Minimize the time it takes to find them
■ Interaction cost integrated over time
Personalization
● Personalization

6. Ordering of the titles in each row is personalized
From what shows to recommend

7. Selection and placement of the row types is personalized
... To how to construct the page

8. Personalized images.
Profile 1 Profile 2
... To what images to select

9. Personalized messages.
... To what email messages to send

10. Everything is a recommendation!

11. Personalization: A note on
practicality
● When the catalog size is very large, recommendations are the only saving grace.

12. Just these ? Show them all...

13. Convinced ?

14. Personalization and Its Design
Considerations
● A good Recommender Systems should therefore consider:
○ What to recommended
■ Relevant content appeals to our members
○ How to recommended
■ Appealing presentation increases their joy

15. What and How to Model
● We try to model
○ User’s taste
○ Context
■ Time
■ Device
■ Country
■ Language
■ …
○ Difference in catalogue and local tastes
■ What is popular in US may not be popular in India
■ Not available != Not Popular
○ Presentation

16. What and How to Model
● We try to model
○ User’s taste
○ Context
■ Time
■ Device
■ Country
■ Language
■ …
○ Difference in catalogue and local tastes
■ What is popular in US may not be popular in India
■ Not available != Not Popular
○ Presentation

17. Basic Intuition behind Recommender
System Algorithms

18. Basic Intuition
● Imagine you walked into a room full of movie enthusiasts, from all over the world,
from all walks of life, and your goal was to come out with a great movie
recommendation.
● Would you obtain popular vote ? Would that satisfy you ?

19. Basic Intuition
● Now consider forming groups of people with similar taste based on the videos that
they previously enjoyed.

23. Basic Intuition
● Describe yourself using what you have watched.
● Try to associate yourself with these groups and obtain a weighted “personalized”
popularity vote.

24. Latent Models for
Recommendation

25. ● Linear Family
○ Latent Factor Models -- Matrix Factorization (MF)
○ Latent Dirichlet Allocation (LDA)
● Non Linear Family -- Deep Learning
○ Variational Autoencoder
○ Feedforward Neural Networks
○ Sequential Neural Networks (RNNs)
○ Convolutional Neural Networks
Latent Models

26. Matrix Factorization: Training

27. Matrix Factorization: Training

28. Matrix Factorization: Scoring
P
Q
Users
Videos
Linear Factor
Interaction
User factor
for user-i
Item factor for
video-j

29. Topic Models (Latent Dirichlet Alloc)
K
U
P
α θ φt v
β
Total
Topics
Taste
Convex Combinations of
topics proportions and movie
proportions within topic

30. Topic Models (LDA): Scoring
P
Q
Users
Videos Distribution
over topics
for user-i
Topic
Conditional
distribution for
video-j

31. Variational Autoencoders
zu
u
Taste
fθ
𝞵 𝞼
u
Encoder
Decoder
fѰ
fѰ
DNN
Soft-max over entire vocabulary
Liang et al. (2018), Variational Autoencoders for Collaborative Filtering, WWW.

32. Next Play Models

33. Neural Multi Class Models
play (t-n)
...
play (t-1)
cntxt
Soft-max over entire vocabulary
play
(t-n)...
play
(t-1)cntxt
Soft-max over entire vocabulary
N-GRAM BoW-n
Feed
Forward User,Cntxt
P(next-video | <user, cntxt>)

34. Neural Multi Class Models
play
(t-1)
cntxt
Soft-max over entire vocabulary
state
(t-1)
RNN Family
play
(t-2)
...
play
(t-1)
Soft-max over entire vocabulary
cntxt
play
(t-4)play
(t-3)
play
(t-n)play
(t-n+1)
CNN Family
state
(t)
Recurrent
Convolutn
P(next-video | <user, cntxt>)

35. Results (internal Netflix dataset)

36. Interpreting a CNN CF Model
● Deeper CNN layers have discovered higher level features in images:
○ Edges
○ Faces etc
● What would a CNN learn if it is trained on user-item interaction dataset?
○ Can it discover semantic topics ?

37. Interpreting a CNN CF Model
HorroR Filter
Kids Filter
Narcotics Filter

38. Concluding Remarks

39. Take Away Points
● Linear models
○ Presented a unified view of various latent factor models
○ Discussed limited modeling capacity ⇒ inferior prediction power
● Non-Linear (Deep Learning) models
○ Encoding of rich nonlinear user item interaction ⇒ superior prediction power
○ Discussed how VAEs can be thought of as non linear LDA
○ Showcased how ‘Next Play models’ model directly the task at hand

40. Some challenging Problems
● Modeling User Context in these frameworks
● Modeling differences in local tastes and catalog differences
○ How to impute for missing plays
○ Censored cross entropy loss
● Unification of various recommender systems
○ Movie recommender, Page builder, Art work selector and many more
● How to minimize production bias
○ Correlation is not Causation
● Long term reward -- User joy
○ Reinforcement Learning

41. Thank you
Anoop Deoras: adeoras@netflix.com
@adeoras