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Talk given by Etienne Sanson, Criteo during the RecsysFR meetup on October 6th 2016.
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RecsysFR: Criteo presentation
1.
RecSys FR: 4th
Session 6th October 2016 Etienne Sanson – manager R&D Engine
2.
About Criteo 1
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 :
8.
About ML@Criteo 2
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
13.
Thank you! …and we’re
hiring!
Hinweis der Redaktion
How to connect? We have some data that allows for deterministic match, but we also have to build a probabilistic match
Hashing trick, one-hot encoding, distributed optimization Tradeoff between freshness of model and historical data Irma
How to connect? We have some data that allows for deterministic match, but we also have to build a probabilistic match
trade-off fast-cheap / expensive-truth
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