Dr. Tobias Lang from Zalando adtech lab held this presentation on "Deep Learning for personalization in large-scale e-commerce applications" on the BIG DATA & NO SQL MEETUP in the Zalando adtech lab Office on 18th May 2017

1. DEEP LEARNING
FOR PERSONALIZATION IN
LARGE-SCALE
E-COMMERCE
APPLICATIONS
BIG DATA & NOSQL MEETUP
TOBIAS LANG
ZALANDO ADTECH LAB
HAMBURG
18 MAY 2017

2. 2
FOUNDED IN 2008
+10.000 EMPLOYEES
7 TECH HUBS
+1300 TECHNOLOGISTS
15 COUNTRIES
21.5 MILLION APP DOWNLOADS
+19 MILLION CUSTOMERS
(2016)

3. 3
ZALANDO

4. 4
DEEP LEARNING
Sequence of multiple layers
● matrix multiplications
● non-linear activation functions

5. 5
DEEP LEARNING
0.2 -0.5 0.1 2.0
1.5 1.3 2.1 0.0
...
Weight matrix W ∈ ℝ(4,6)
56
231
24
2
Input x ∈ ℝ4
56 * 0.2 + 231 * (-0.5) + 24 * 0.1 + 2 * 2.0 = -76.3
...
Sequence of multiple layers
● matrix multiplications
● non-linear activation functions

6. 6
DEEP LEARNING
0.2 -0.5 0.1 2.0
1.5 1.3 2.1 0.0
...
0.3 -0.5 -0.3 1.7
1.6 1.8 2.2 -0.4
...
“Learning”:
● adapting weight matrices
● gradient descent
(backpropagation)
t t + 1

7. 7
DEEP LEARNING
● Deep learning theory is old.
○ Backpropagation in neural nets: Werbos, 1972
○ Convolutional neural nets: LeCun, 1989
○ Long short-term memory RNNs: Hochreiter & Schmidhuber, 1997
● Neural nets and deep learning were “dead” around 2000.
● Hype since ~2010
○ Drastic improvements in vision and speech
○ Winning data competitions (Kaggle, imagenet etc.)
○ Nota bene: still very far from general artificial intelligence

8. 8
● A lot more data (“big data”)
● Better hardware (GPUs)
● Algorithmic improvements
● Big-data examples:
○ Speech recognition, object recognition:
Google, Facebook, Alibaba etc. have billions of training points
○ Game of Go:
robot played 100M of games against itself, using a large-scale farm of GPUs
(vs. Lee Sedol: 50k games, 1 brain)
WHY THE COMEBACK?

9. 9
LIMITS OF DEEP LEARNING
Karpathy (2012): The state of Computer Vision and AI: we are really, really far away.
http://karpathy.github.io/2012/10/22/state-of-computer-vision/

10. 10
LIMITS OF DEEP LEARNING
Lake, Ullman, Tenenbaum, Gershman (2016):
Building machines that learn and think like people
Current DL only does mere pattern recognition.
→ Need to learn causal models of the world
that support explanation and reasoning
Current DL requires big data.
→ Need to learn from small data
Prior knowledge: intuitive theories of physics and psychology
compositionality and learning-to-learn

11. 11
DEEP LEARNING AT ZALANDO
Time
Ad-Click
8:23 10:35 10:48 10:49 20:22
Now
Product
View
Product
View Order?
Cart
Addition
ORDER WITHIN NEXT 7 DAYS?CONSUMER HISTORY

12. 12
RECURRENT NEURAL NETWORKS FOR CONSUMER HISTORY
Time
Ad-Click
8:23 10:35 10:48 10:49
Product
View
Product
View
Cart
Addition
CONSUMER HISTORY
RNN
Cell
RNN
Cell
RNN
Cell
RNN
Cell

13. 13
RECURRENT NEURAL NETWORKS FOR CONSUMER HISTORY
Time
Ad-Click
8:23 10:35 10:48 10:49
Product
View
Product
View
Cart
Addition
CONSUMER HISTORY
RNN
Cell
RNN
Cell
RNN
Cell
RNN
Cell
0.23
-0.8
0.03
...
Consumer’s
latent state
0.43
-0.53
0.01
...
0.55
-0.62
0.11
...
-0.22
-0.72
0.31
...
0.23
-0.8
0.03
...
Learned
features

14. 14
LONG SHORT-TERM MEMORY (LSTM) CELLS
Images with equations from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Cell state
Long-term memory

15. 15
LONG SHORT-TERM MEMORY (LSTM) CELLS
Images with equations from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Step 1
Forget old
information?
Cell state
Long-term memory
Step 2
Which new
information
to consider?
Step 3
Update long-term
memory
Step 4
Output

16. 16
LONG SHORT-TERM MEMORY (LSTM) CELLS
Images with equations from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Step 1
Forget old
information?
Cell state
Long-term memory
Step 2
Which new
information
to consider?
Step 3
Update long-term
memory
Step 4
Output
“Learning”: parameters are adapted during training.

17. 17
RNN PREDICTIONS
RNN cells
(consumer’s
latent state)
Consumer history

18. 18
RNN PREDICTIONS
Probability
to order
after event
Consumer history

19. 19
AD-TECH LAB HAMBURG
DATA AGGREGATION WORKFLOW
Terabytes of data
EVENT DATA
FEATURE
MATRIX
GBs
JOURNEY ENCODING DEEP LEARNING
Spark Torch, Tensorflow

20. 20
PRODUCTION SETTING
adtech lab Hamburg
Data Storage
AWS S3
Data Aggregation
AWS EMR
Event Tracking
Model Training
AWS EC2
Prediction
AWS EC2
AWS
Pipelines
AWS
Pipelines
AWS
Pipelines

21. 21
FASHION DNA
Project fashion articles into abstract space ℝn
Article inputs: photos, product attributes, sales data
Projection via deep learning
(convolutional neural network)
(deep neural
network)
Brown
Women
Cardigan
..

22. 22
FASHION DNA: NEAREST NEIGHBORS
Sales data yield context.

23. 23
FASHION DNA: ARTICLE UNIVERSE
https://github.com/cbracher69/fashion-tsne-map/blob/master/kdd.tsne_map.large.jpg

24. 24
FASHION DNA: ARTICLE UNIVERSE

25. 25
MORE INFORMATION
RNNs (Zalando adtech lab Hamburg):
● Lang, Rettenmeier: Understanding Consumer Behavior with Recurrent Neural Networks. MLRec 2017.
● Zalando Tech-Blog:
https://tech.zalando.com/blog/deep-learning-for-understanding-consumer-histories/
https://tech.zalando.com/blog/deep-learning-in-production-for-predicting-consumer-behavior/
Fashion DNA (Zalando Research Berlin):
● Bracher, Heinz, Vollgraf: Fashion DNA: Merging content and sales data for recommendation and article
mapping. KDD Workshop 2016.

26. 26
Deep learning requires big-data contexts.
We use deep learning to personalize our fashion
platform experience.
We use deep learning to better understand and use
our fashion article universe.
CONCLUSIONS

27. 27 adtech lab Hamburg
https://www.meetup.com/de-DE/Zalando-Tech-Events-Hamburg/

28. THANK YOU FOR YOUR ATTENTION
TOBIAS LANG
TOBIAS.LANG@ZALANDO.DE
ZALANDO ADTECH LAB
HAMBURG