1.
DEEPER THINGS
Aish Fenton
@aishfenton
Sudeep Das
@datamusing

2.
Popular On Netflix

3.
● 1999-2005: DVD
● Netflix Prize (2006-2009):
○ > 10% improvement, win $1,000,000
● Top performing model(s) ended up being Matrix
Factorization (SVD++, Koren, et al)
● We’ve moved on, but MF is still a much-used
foundational method
Netflix,Recommendation Systems, ML

4.
USERSITEMS
0 1 0 1 0
0 0 1 1 0
1 0 0 1 1
0 1 0 0 0
0 0 0 0 1
Traditional Recommendation Setup

5.
U≈R
V
Matrix Factorization

6.
U≈R
V
Matrix Factorization

7.
GOING DEEPER

8.
If MF were a Neural Network…
V
U

9.
Mean
Squared Loss?
Now we can go deeper
V
U

10.
The naive approach …
V
U
The knee-jerk reaction would be to add
multiple fully connected layers here.
Does that work?
Loss
function

11.
Loss
function
… isn’t always the best!
V
U

12.
But NNs open up many other possibilities ...
Softmax
BOW -average
/ N-gram /
Sequence
Feed-forward/
LSTM
Input
interactions
(X)
p(Y)
V

13.
Adding time and context
Softmax
Sequence
LSTM
Input
interactions
(X)
p(Y)
2018-12-2319:32:10
2018-12-2412:05:53
2019-01-0215:40:22
V

14.
Offline ranking metrics

15.
Loss function
?
Adding heterogeneous side information ...
V
U
User Metadata:
e.g. Country, Preferred Language
Item Metadata:
e.g. Country of origin, synopsis
embedding, boxart embedding

16.
Rich hierarchical side information cannot be easily
embedded into Euclidean space
Politically Incorrect Stand
Up
Stand Up
Comedies
Romantic
Comedies
Raunchy Stand Up
Feel Good Romantic
Comedies
Romantic Comedies based
on Books
Women Who Make Us
Laugh
Alltagging
metadata
Documentaries

17.
STILL DEEPER

18.
Riddle: What’s so vast it can’t
fit in a deep neural network?

19.
Bright
Birdbox
Sandra Bullock
Mr and Mrs. Smith
Will Smith

20.
EXPONENTIALGROWTH

21.
We need to leave Euclidean
space and go into Hyperbolic
Space

22.
EXPONENTIAL GROWTH WITH R
NEG CURVATURE

23.
V
Uu
v

24.
u
v
dist(u,v)
gradient of operations
V
U

25.
Differential geometry provides the answer!

26.
EXPONENTIALGROWTH

27.
QUESTIONS ?