Deep Learning for Recommender Systems

Marcel Kurovski Karlsruhe, October 25th 2017

2

About Me§ Industrial Engineer (M.Sc.)§ Data Scientist at inovex§ Machine Learning – focus on Deep Learning§ Masterthesis:

Deep Learning for Recommender Systems:Joint Learning of Preference and Similarity

Marcel Kurovski

3

Agenda

1. Motivation

2. State-of-the-Art

3. VehicleRecommendationswith Deep Learning

4. ACM RecSysConference 2017

5. Discussion

4

Annual Data Sphere increases exponentially

International Data Corporation: Data Age 2025 study, April 2017

Informationà Humans

Processing Capacity

5

Information Overload

https://www.linkedin.com/pulse/its-information-overload-filter-failure-productivity-industry-zayats/

“It‘s not information overload.It‘s filter failure."

- Clay Shirky

7

Collaborative Filtering

? 1 1 1

? 1 ? ?

1 1

m

Users

n Items

3

1

2

3

1

2

2

1

3

4

1 2 3 4

8https://www.slideshare.net/MrChrisJohnson/algorithmic-music-recommendations-at-spotify/10-Collaborative_Filtering10HeyI_like_tracks_P

Collaborative Filtering

9https://buildingrecommenders.wordpress.com/2015/11/18/overview-of-recommender-algorithms-part-2/

Matrix Factorization

10

Cold Start

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

11

Recommender Systems for IF

SPARSITY

12adapted from http://www.kdnuggets.com/2016/02/nine-datasets-investigating-recommender-systems.html

Sparsity Comparison

13adapted from http://www.kdnuggets.com/2016/02/nine-datasets-investigating-recommender-systems.html

Sparsity ComparisonMovieLens 1M: 4.26% MovieLens 20M: 0.53%

Last.fm: 0.28% Vehicles All: 0.0046%

14

Content-based Filtering

? 1 1 1

? 1 ? ?

1 1

m

Users

n Items

age

gender

history

mileagemodelcolor

3

1

2

3

1

2

2

1

3

4

1 2 3 4

15

“Deep Learning becomes a general-purpose solution fornearly all learning problems."

- Covington et al.

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Google Trends forDeep Learning

16

Motivation: Deep Learning for RecSys

Information Overload

Information Filtering

RecommenderSystems

Learning Problem

Deep Learning

17

Agenda

1. Motivation

2. State-of-the-Art

3. VehicleRecommendations withDeep Learning

4. ACM RecSysConference 2017

5. Discussion

18

Recommendations are everywhere

19

„The company reported a 29% salesincrease to $12.83 billion [...]Amazon has integratedrecommendations into nearly everypart of the purchasing process fromproduct discovery to checkout.“

http://fortune.com/2012/07/30/amazons-recommendation-secret/

20

„Our recommender system is usedon most screens of the Netflixproduct beyond the homepage, andin total influences choice for about80% of hours streamed at Netflix. The remaining 20% comes fromsearch [...]“

Gomez-Uribe, Carlos A. and Hunt, Neil: The Netflix Recommender System: Algorithms, Business Value, and Innovation (2015)

Suche

EmpfehlungenRecommendations

Search

21DLRS: Deep Learning based Recommender Systems

Domains and Types for DLRS

DNNs

CNNs

RNNs

AEs

Sonst.

Sonst.

2013

2016

2017

2015

2009

2015 2015 2015

2017

2015

2016

2016

22

DNNs for Video-Recommendations (1)

23Covington, Paul, Jay Adams, and Emre Sargin: Deep neural networks for youtube recommendations (2016)

DNNs for Video-Recommendations(2)

24Covington, Paul, Jay Adams, and Emre Sargin: Deep neural networks for youtube recommendations (2016)

DNNs for Video-Recommendations(3)

Deep Candidate Generation Deep Ranking

25Cheng, Heng-Tze et al.: Wide and Deep Learning for Recommender Systems (2016)

https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html

Wide and Deep Learning for App-Recos (1)

26

Wide and Deep Learning for App-Recos (2)

Cheng, Heng-Tze et al.: Wide and Deep Learning for Recommender Systems (2016)

https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html

DeepComponent Wide

ComponentEmbeddings

27

Agenda

1. Motivation

2. State-of-the-Art

3. VehicleRecommendationswith Deep Learning

4. ACM RecSysConference 2017

5. Discussion

28

Vehicle Recommendations: End-to-End Approach

CandidateGeneration

Serving Ranking

Preprocessing ClassifierTrainingData

29

Vehicle Recommendations: Technologies

Locally OptimizedProduct Quantization

HardwareGPU-Server

NVIDIA Tesla K804x Intel Xeon 3.5 GHz64GB RAM, 850GB Disk

AWS Instances

30

Vehicle Recommendations: Data

Users & InteractionsRegistered UsersSample Size: 100,000 UsersEvents: View, Bookmark, Contact

Time-basedTrain-Test-Split

CW14

CW15

CW16

CW17

CW18

April 2017 May

Training Test

85 : 15

31https://medium.com/towards-data-science/deep-learning-4-embedding-layers-f9a02d55ac12

What does ‘Embedding‘ actually mean?

0. blue 0

1. green 0

2. red 0

3. yellow 0

4. orange 0

5. black 1

6. white 0

7. brown 0

1

0

1

binaryEmbedding

One-Hot-Encoding

32

33

categorical features

one-many-encoding one-hot-encoding

feature valuesucat icat

eclimatisation

icont

embeddinguser

consumption first_reg price...

embeddingi, cont

ucont

embeddingu,cont

...

outlier removal

z-normalisation

ELU (256)

ELU (128)

ELU (64)

Deep Component

Wide Component

cross user-item transformations

embeddingitem

...

...

climatisation color

ecolor etransmission

transmission

OutputProbability that user ulikes vehicle i

meanconsumption meanprice

stddevconsumption stddevprice

...

concatenate concatenate

outlier removal

z-normalisation

Pre

pro

cess

ing

Em

bed

din

gW

ide

and

Dee

p

34

Vehicle Recommendations: End-to-End Approach

CandidateGeneration

Serving Ranking

Preprocessing ClassifierTrainingData

✓ ✓

35

Vehicle Recommendations: Ranking

Target: Rank Candidates descendantly by interaction probability

user0

...

userm

itemm,0

...itemm,T

user-specificCandidate Lists

user-specifick-Rankings

𝑘 ≤ 𝑇

itemm,0

...itemm,k

36

Vehicle Recommendations: End-to-End Approach

CandidateGeneration

Serving Ranking

Preprocessing ClassifierTrainingData

✓ ✓

✓ ✓

37

Results: DLRS Recommendation Relevance

0,25%

0,35%

0,45%

0,55%

0,65%

0,75%

0,85%

k = 1 k = 5 k = 10

MA

P@

k

CF (⍺=0.03, d=100)

Hybrid CF-CBF (⍺=0.03, d=100)

Hybrid CF-CBF (⍺=0.03, d=700)

DL (multi-cos)

+20%

+65%

38

Agenda

1. Motivation

2. State-of-the-Art

3. VehicleRecommendationswith Deep Learning

4. ACM RecSysConference 2017

5. Discussion

39

40Twitter: @domonkostikk

ACM RecSys Conference 2017

627 Participants

43 Countries

„Accuracy doesn‘t matter – impact does!“

„Try to not useMovieLens“

„People are most curiousabout themselves“

41Quadrana, Massimo et al.: Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks (2017)

RNNs for Video and Job Recommendations

42

"We can only see a short distanceahead, but we can see plentythere that needs to be done."

- Alan Turing

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References[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] Wang, Hao, Wang, Naiyan, Yeung, Dit-Yan. “Collaborative Deep Learning for Recommender Systems“ Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015

[3] Cheng, Heng-Tze, et al. "Wide & deep learning for recommender systems." Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016.

[4] Covington, Paul, Jay Adams, and Emre Sargin. "Deep neural networks for youtube recommendations." Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[5] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016.

[6] Heaton, Jeff. Artificial Intelligence for Humans: Deep Learning and Neural Networks. 2015.

[7] Ricci, Francesco and Rokach, Lior and Shapira, Bracha. Recommender Systems Handbook. Springer-Verlag. 2015

[8] Abadi, Martín, et al. "Tensorflow: Large-scale machine learning on heterogeneous distributed systems." arXiv preprint arXiv:1603.04467 (2016).

[9] Loni, Babak, et al. "Bayesian Personalized Ranking with Multi-Channel User Feedback." Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.

[10] Kalantidis, Yannis, and Yannis Avrithis. “Locally optimized product quantization for approximate nearest neighbor search.“ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.

[11] 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

Thank You

Marcel Kurovski

Big Data Scientist

inovex GmbH

Kupferhütte 1.13,

Schanzenstr. 6-20

51063 Cologne

marcel.kurovski@inovex.de

0173 3181 088