The document presents an extensive overview of deep learning applications in recommender systems, highlighting their importance in various industries like e-commerce and streaming services. It discusses the technical approaches, challenges such as data sparsity and cold start problems, and the role of deep learning to enhance recommendation relevance. Furthermore, it emphasizes the scalability of these systems and offers insights into future research directions in recommender systems.

1. Deep Learning for Recommender Systems
Marcel Kurovski O‘REILLY AI, New York, April 18th 2019
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!
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2. 2
Marcel Kurovski
Data Scientist
Recommender Systems
Deep Learning
Reinforcement Learning
Data Science to Production

3. 3
1. Motivation
2. Basics and Overview
3. Deep Learning for Vehicle Recommendations
4. Scalability and Production
Agenda

4. 4
Annual Data Sphere increases exponentially
International Data Corporation: Data Age 2025 study, April 2017
Information Load
à Humans
Human Processing
Capacity

5. 5
Information and Choice Overload
https://www.linkedin.com/pulse/its-information-overload-filter-failure-productivity-industry-zayats/
https://en.wikipedia.org/wiki/Clay_Shirky
“It‘s not information overload. It‘s filter failure." - Clay Shirky

6. 6
- Covington et al.
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Google Trends for
“Deep Learning“
“Deep Learning becomes a
general-purpose solution for
nearly all learning problems."

7. Recommendations are everywhere
7

8. http://fortune.com/2012/07/30/amazons-recommendation-secret/8
„The company reported a 29%sales
increase to $12.83 billion [...]
Amazon has integrated
recommendations into nearly every part
of the purchasing process from product
discovery to checkout.“

9. 9 Gomez-Uribe, Carlos A. and Hunt, Neil: The Netflix Recommender System: Algorithms, Business Value, and Innovation (2015)
„Our recommender system […]
in total influences choice for about
80% of hours streamed at Netflix.
The remaining 20% comes from search
[...]“
Suche
Empfehlungen
Recommendations
Search

10. 10 Gomez-Uribe, Carlos A. and Hunt, Neil: The Netflix Recommender System: Algorithms, Business Value, and Innovation (2015)
„Reduction of monthly churn both increases the lifetime value of an existing
subscriber, and reduces the number of new subscribers we need to acquire to
replace cancelled members.
We think the combined effect of
personalization and recommendations
save us more than $1B per year.“
Suche
Empfehlungen

11. 11
1. Motivation
2. Basics and Overview
3. Deep Learning for Vehicle Recommendations
4. Scalability and Production
Agenda

12. Interactions
12
m
users
1 1 1
? 1 ? ? 1 ?
1 1 1
1 1 1
n items

13. Collaborative Filtering
13
Muse
Arctic Monkeys
The Killers
Coldplay
Bloc Party
Check out
Bloc Party
Check out
Muse

14. https://buildingrecommenders.wordpress.com/2015/11/18/overview-of-recommender-algorithms-part-2/
Matrix Factorization
14

15. 15
Recommender Systems for IF
SPARSITY

16. 16
Cold Start
http://www.yusp.com/wp-content/uploads/2015/07/cold-start-problem-recommender-systems-1.jpg

17. 17
Item Information User Information Contextual
Information
Types of Content

18. Content-based Filtering
18
1 1 1
? 1 ? ? 1 ?
1 1 1
1 1 1
model
color
mileageage
gender
income

19. 19
Capture Nonlinear
Relationships
Reduce Feature
Engineering Effort
Flexible and Holistic
Approach
Improve Predictive
Capability
Deep Learning for Recommender Systems (DLRS)

20. see Slide on References, Details: https://bit.ly/2WuS4Zq
Domains and Types for DLRS
20
DNNs
CNNs
RNNs
AEs
Other
Other
2017
2018
2009
2015 2015
2017
2016
2015
2018
2016
2013
2018
2018
2017
2018
2018
2018
2018
2018
2017
https://bit.ly/2WuS4Zq

21. Cheng, Heng-Tze et al.: Wide and Deep Learning for Recommender Systems (2016)
Wide and Deep Learning for App-Recos
Combine Memorization and Generalization
21

22. Cheng, Heng-Tze et al.: Wide and Deep Learning for Recommender Systems (2016)
Wide and Deep Learning for App-Recos
Combine Memorization and Generalization
22
Deep
Component
Embeddings
Wide
Component

23. Session-based Recommendations
Leverage Sequential Information to Improve Relevance
www.netflix.com23
t
DESIGNATED
SURVIVOR
DARK
DESIGNATED
SURVIVOR
DARK
› HOUSE OF CARDS
› STRANGER THINGS
› HOUSE OF CARDS
› STRANGER THINGS
STRANGER
THINGS
HOUSE OF
CARDS

24. Session-based Recommendations
Leverage Sequential Information to Improve Relevance
Quadrana et al.: Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks (2017)24

25. 25
1. Motivation
2. Basics and Overview
3. Deep Learning for Vehicle Recommendations
4. Scalability and Production
Agenda

26. Vehicle Recommendations: End-to-End Approach
26
Candidate
Generation
Serving Ranking
Preprocessing Classifier
Training
Data
1.
2.
3.

27. Vehicle Recommendations: Technologies
Frameworks and Hardware for Training and Inference
27

28. Vehicle Recommendations: Data
28
Users & Interactions
Registered Users
Sample Size: 100,000 Users
Events: View, Bookmark, Contact
Time-based
Train-Test-Split
CW
14
CW
15
CW
16
CW
17
CW
18
April 2017 May
Training Test
85 : 15

29. adapted from http://www.kdnuggets.com/2016/02/nine-datasets-investigating-recommender-systems.html
Sparsity Comparison
29
MovieLens 1M: 4.26% MovieLens 20M: 0.53%
Last.fm: 0.28% Vehicles All: 0.0046%
~8M interactions between 100k users and 1.7M items

30. Approach: Preprocessing (1)
30
Technical
§ Data Extraction (SQL, HDFS)
§ Data Type Conversions
§ User and Item ID Contiguation
§ Weekly Profile Overlap
§ User Set Sampling
Content-related
§ Category-based Negative
Sampling
§ Assign Binary Labels {0, 1}
§ Outlier Removal and Feature
Normalization
§ User Profile Feature
Conversion

31. Approach: Preprocessing (2)
31
0.4 0.4 0 0 0.2 0 0 0
∅ = 9,000€ # = 1,817€
uprice
ucolor
8,500€
7,000€
10,000€
7,500€
12,000€
deterministic stochastic

32. 32
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!
"

33. 33
categorical features
many-hot-encoding one-hot-encoding
feature values
ucat icat
eclimatisation
icont
embeddinguser
consumption first_reg price...
embeddingi, cont
ucont
embeddingu,cont
...
outlier removal
z-normalisation
ELU (256)
ELU (128)
ELU (64)
embeddingitem
...
...
climatisation color
ecolor etransmission
transmission
Probability that user u
likes vehicle i
meanconsumption meanprice
stddevconsumption stddevprice
...
concatenateconcatenate
PreprocessingEmbeddingDeepComponent
outlier removal
z-normalisation

34. 34
categorical features
many-hot-encoding one-hot-encoding
feature values
ucat icat
eclimatisation
icont
embeddinguser
consumption first_reg price...
embeddingi, cont
ucont
embeddingu,cont
...
outlier removal
z-normalisation
ELU (256)
ELU (128)
ELU (64)
embeddingitem
...
...
climatisation color
ecolor etransmission
transmission
Probability that user u
likes vehicle i
meanconsumption meanprice
stddevconsumption stddevprice
...
concatenateconcatenate
PreprocessingEmbeddingDeepComponent
outlier removal
z-normalisation
UserNet ItemNet
RankNet

35. minimize
minimize
Adam Optimizer: Stochastic Gradient Descent with adaptive learning rate and adaptive momentum
Approach: Classifier Training
35 35
RankNet
eu
u
UserNet
ei
i
ItemNet
p ( i | u )
class_loss
sim_loss
Adam
Optimizer
Adam
Optimizer

36. Approach: Cost Functions
36
1
2
sim_loss

37. https://erikbern.com/2016/06/02/approximate-nearest-news.html
Candidate Generation
Apply Approximate Nearest Neighbor Search to Embeddings
37
x1
x2
5 approximate itemnearest neighbors search user embedding

38. Intuition: Embedding Similarity Regularization
38
x1
x2
x3
x1
x2
u
i euei
embedding
✓
✘
⍺
⍺

39. Vehicle Recommendations: Ranking
Rank Candidates by Descending Interaction Probability p(i|u)
39
… ~ 1.7 M Vehicles
1.
2.
3.
1.
2.
3.
RankNet

40. Vehicle Recommendations: Serving
Present Top-k Recommendations to the User
40
1.
2.
3.

41. 41
Recommendation Channels
Main Page Favorites Similar Vehicles

42. Vehicle Recommendations: End-to-End Approach
42
Candidate
Generation
Serving Ranking
Preprocessing Classifier
Training
Data
1.
2.
3.
✓ ✓
✓ ✓
✓

43. Results: DLRS Recommendation Relevance
43 MAP: mean average precision, comparative results after optimization of hyperparameters
0,20%
0,30%
0,40%
0,50%
0,60%
0,70%
0,80%
0,90%
1,00%
1,10%
k = 1 k = 5 k = 10 k = 30 k = 100
MAP@k
Deep Learning
Hybrid CF-CBF (d=700)
CF (d=100)
1.10%
1.00%
0.90%
0.80%
0.70%
0.60%
0.50%
0.40%
0.30%
0.20%
"
+73%
+143%

44. 44
1. Motivation
2. Basics and Overview
3. Deep Learning for Vehicle Recommendations
4. Scalability and Production
Agenda

45. Deploying Vehicle Recommendations at Scale
45
item
storage
embeddings
RankNet
UserNet
ItemNet
ANNOY
ANN index
Candidate ServiceRanking Service
Webservice
User Profile API
Recommendation Service
k recommendations
rank candidates
{ei} for eu
get u
get eu
get T
candidates
{ei}
get i
get ei
index
all ei
ANN
search

46. 46
Deep Learning Solved – What’s next?
http://dlrs-workshop.org/wp-content/uploads/2018/10/dlrs2018_welcome.pdf

47. 47
Sequence-based und
Sequence-aware
Causal Inference
(Deep) Reinforcement
Learning
Current Trends in Recommender Systems Research

48. 48
"We can only see a short distance ahead,
but we can see plenty there that needs to
be done."
- Alan Turing

49. Thank You
Marcel Kurovski
Data Scientist
inovex GmbH
Kupferhütte 1.13,
Schanzenstr. 6-20
51063 Cologne
marcel.kurovski@inovex.de
+49 173 3181 088
Dr. Florian Wilhelm
Principal Data Scientist
Julian Hatzky
Data Science Working Student

50. References
50
[1] Quadrana, Massimo, Karatzoglou, Alexandros, Hidasi, Balázs, Cremonesi, Paolo. “Personalizing Session-based Recommendations with Hierarchical Recurrent Neural
Networks“ Proceedings of the 11th ACM Conference on Recommender Systems. 2017
[2] Cheng, Heng-Tze, et al. "Wide & deep learning for recommender systems." Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016.
[3] Covington, Paul, Jay Adams, and Emre Sargin. "Deep neural networks for youtube recommendations." Proceedings of the 10th ACM Conference on Recommender
Systems. ACM, 2016.
[4] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016.
[5] Heaton, Jeff. Artificial Intelligence for Humans: Deep Learning and Neural Networks. 2015.
[6] Ricci, Francesco and Rokach, Lior and Shapira, Bracha. Recommender Systems Handbook. Springer-Verlag. 2015
[7] Reinsel, David, Gantz, John, Rydning, John. “Data Age 2025: The Evolution of Data to Life-Critical Don't Focus on Big Data; Focus on the Data That's Big“ International
Data Corporation (IDC). 2017
[8] Gomez-Uribe, Carlos A. and Hunt, Neil: The Netflix Recommender System: Algorithms, Business Value, and Innovation. 2015
[9] JP Mangalindan: Amazon's recommendation secret. 2012
[10] Christ Johnson: Algorithmis Music Discovery at Spotify. 2014
[11] Maya Hristakeva: Overview of Recommender Algorithms - Part 2. 2015
[12] Alex Gude: The Nine Must-Have Datasets for Investigating Recommender Systems. 2016
[13] Erik Bernhardsson: Approximate nearest news. 2016
[14] Balász Hidasi. 3rd Workshop on Deep Learning for Recommender Systems. 2018
[15] CartStack LLC: Comparison could be killing your online business. 2017
[16] Marina Zayats: “It‘s not information overload; it‘s filter failure.“ Productivity in the Industry 4.0. 2016

51. References – Want to read more?
51
https://bit.ly/2WuS4Zq

52. 52

53. Thank You! Questions or Comments?
53