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RecSys FR: 4th Session
6th October 2016
Etienne Sanson – manager R&D Engine
About Criteo
1
3 | Copyright © 2016 Criteo
Our mission
TARGET THE
RIGHT USER
AT THE
RIGHT TIME
WITH THE RIGHT
MESSAGE
4 | Copyright © 2016 Criteo
Key Figures
16 000
PUBLISHERS
90%
RETENTION RATE2
+130
COUNTRIES
LISTED ON THE
NASDAQ
SINCE
OCTOBER 2013
R&D REPRESENTS 21%
OF THE WORKFORCE
2000
EMPLOYEES
21
BILLIONS $3
11 000
ADVERTISERS
1.19 bn€1
31
OFFICES
1: REVENUE IN 2015
2: ANNUAL RATE 2015
3: $ OF TURNOVER GENERATED TO OUR CLIENTS - TURNOVER POST-CLICK WW FROM JANUARY TO DECEMBER
2015
5 | Copyright © 2016 Criteo
Revenue Growth
2009 2010 2011 2012 2013
22M$
86M$
199M$
349M$
589M$
988M$
2014
1,3MM$
2015
6 | Copyright © 2016 Criteo
GENERAL CONCEPT
Users visit an
advertiser’s website
1
Criteo identifies the users
(via cookies)
2
Users leave the advertiser’s website
& browse publisher on the Internet
3
Criteo identifies users on
these pages
(via cookie)
4
Criteo displays an advertising
banner, personalized for
each user
5
Click through directly
to the advertiser’s
page
6
@
Retargeting principles
7 | Copyright © 2016 Criteo
Infrastructure Key Figures
Sunnyvale
2 PoP
500 kVA
1 559 Servers
New York
2 PoP
930 kVA
2 625 Servers
Hong Kong
2 PoP
472 kVA
2185 Servers
Paris
4 Pop
1 800 kVA
3 625 Servers
Amsterdam
2 PoP
+2 500 kVA
3 609 Servers
Tokyo
2 PoP
455 kVA
2 564 Servers
Shanghai
1 PoP
200 kVA
931 Servers
World Wide
15 PoP
6,8 MVA
17 098 Servers
> 55Gbps
+ 2.5M req/s
Hosting Global Partners :
About ML@Criteo
2
9 | Copyright © 2016 Criteo
Our challenges – Product recommendation
• Select the best ~10 products to show to a user
• >1B users
• Product catalog contains ~1M items, up to 1B
• Time constraints: 20ms
• Combination of offline/online processing steps
• CF
• Product embeddings (word2vec -> prod2vec)
• CNN for image features
What products should
we recommend?
10 | Copyright © 2016 Criteo
Our challenges - Bidding
How much should we
bid for this display?
What is the best
campaign to display?
My company
BUY! BUY! BUY!
BUY!
• Select the best campaign to display and evaluate its
value in a few ms
• Large scale regression models
• >1B daily displays (but few positive examples!)
• >1M parameters
• Distributed optimization (SGD, L-BFGS)
• Feature Engineering
• Transfer learning, FFM, Policy learning
• Marketplace, game theory, auction theory
11 | Copyright © 2016 Criteo
Our challenges – X-device
• Build a huge graph (Billions of nodes/edges):
• Nodes = devices
• Edge = the 2 devices belong to the same user
• How to connect 2 devices?
• How to know the ground truth?
• How to keep it stable?
• At scale & taking care about privacy
Who is the user
behind the device?
12 | Copyright © 2016 Criteo
Our challenges – Testing
• We test everything!
• Offline tests / AB Tests
• Infrastructure to perform large-scale tests
• >100K offline tests / year
• >1K AB Tests / year
• Dedicated teams
• Technical / Business Metrics
• Randomization
• Counterfactual evaluation
Thank you!
…and we’re hiring!

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RecsysFR: Criteo presentation

  • 1. RecSys FR: 4th Session 6th October 2016 Etienne Sanson – manager R&D Engine
  • 3. 3 | Copyright © 2016 Criteo Our mission TARGET THE RIGHT USER AT THE RIGHT TIME WITH THE RIGHT MESSAGE
  • 4. 4 | Copyright © 2016 Criteo Key Figures 16 000 PUBLISHERS 90% RETENTION RATE2 +130 COUNTRIES LISTED ON THE NASDAQ SINCE OCTOBER 2013 R&D REPRESENTS 21% OF THE WORKFORCE 2000 EMPLOYEES 21 BILLIONS $3 11 000 ADVERTISERS 1.19 bn€1 31 OFFICES 1: REVENUE IN 2015 2: ANNUAL RATE 2015 3: $ OF TURNOVER GENERATED TO OUR CLIENTS - TURNOVER POST-CLICK WW FROM JANUARY TO DECEMBER 2015
  • 5. 5 | Copyright © 2016 Criteo Revenue Growth 2009 2010 2011 2012 2013 22M$ 86M$ 199M$ 349M$ 589M$ 988M$ 2014 1,3MM$ 2015
  • 6. 6 | Copyright © 2016 Criteo GENERAL CONCEPT Users visit an advertiser’s website 1 Criteo identifies the users (via cookies) 2 Users leave the advertiser’s website & browse publisher on the Internet 3 Criteo identifies users on these pages (via cookie) 4 Criteo displays an advertising banner, personalized for each user 5 Click through directly to the advertiser’s page 6 @ Retargeting principles
  • 7. 7 | Copyright © 2016 Criteo Infrastructure Key Figures Sunnyvale 2 PoP 500 kVA 1 559 Servers New York 2 PoP 930 kVA 2 625 Servers Hong Kong 2 PoP 472 kVA 2185 Servers Paris 4 Pop 1 800 kVA 3 625 Servers Amsterdam 2 PoP +2 500 kVA 3 609 Servers Tokyo 2 PoP 455 kVA 2 564 Servers Shanghai 1 PoP 200 kVA 931 Servers World Wide 15 PoP 6,8 MVA 17 098 Servers > 55Gbps + 2.5M req/s Hosting Global Partners :
  • 9. 9 | Copyright © 2016 Criteo Our challenges – Product recommendation • Select the best ~10 products to show to a user • >1B users • Product catalog contains ~1M items, up to 1B • Time constraints: 20ms • Combination of offline/online processing steps • CF • Product embeddings (word2vec -> prod2vec) • CNN for image features What products should we recommend?
  • 10. 10 | Copyright © 2016 Criteo Our challenges - Bidding How much should we bid for this display? What is the best campaign to display? My company BUY! BUY! BUY! BUY! • Select the best campaign to display and evaluate its value in a few ms • Large scale regression models • >1B daily displays (but few positive examples!) • >1M parameters • Distributed optimization (SGD, L-BFGS) • Feature Engineering • Transfer learning, FFM, Policy learning • Marketplace, game theory, auction theory
  • 11. 11 | Copyright © 2016 Criteo Our challenges – X-device • Build a huge graph (Billions of nodes/edges): • Nodes = devices • Edge = the 2 devices belong to the same user • How to connect 2 devices? • How to know the ground truth? • How to keep it stable? • At scale & taking care about privacy Who is the user behind the device?
  • 12. 12 | Copyright © 2016 Criteo Our challenges – Testing • We test everything! • Offline tests / AB Tests • Infrastructure to perform large-scale tests • >100K offline tests / year • >1K AB Tests / year • Dedicated teams • Technical / Business Metrics • Randomization • Counterfactual evaluation

Notes de l'éditeur

  1. How to connect? We have some data that allows for deterministic match, but we also have to build a probabilistic match
  2. Hashing trick, one-hot encoding, distributed optimization Tradeoff between freshness of model and historical data Irma
  3. How to connect? We have some data that allows for deterministic match, but we also have to build a probabilistic match
  4. trade-off fast-cheap / expensive-truth