New machine learning challenges at Criteo

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A brief overview of the machine learning challenges at Criteo

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New machine learning challenges at Criteo

  1. 1. Copyright © 2015 Criteo New machine learning challenges at Criteo Olivier Koch Engineering Program Manager, Criteo Rythm Meetup June 15, 2016
  2. 2. Copyright © 2015 Criteo Banners… what else? 2 Advertiser Publisher
  3. 3. Copyright © 2015 Criteo Machine learning applications at Criteo • Bidding (2nd price auctions) • Product recommendation • Banner look and feel selection
  4. 4. Copyright © 2015 Criteo Machine learning at Criteo • Supervised learning using standard regression methods / optimization algorithms (SGD, L-BFGS) • Distribution on Hadoop (MapReduce, Spark) • 3B displays / day • 40 PB of data -- 15,000 servers • 7 data centers worldwide
  5. 5. Copyright © 2015 Criteo Data sparsity 10 000 displays lead to 50 clicks lead to 1 sale
  6. 6. Copyright © 2015 Criteo Now what?
  7. 7. Copyright © 2015 Criteo Challenges in online advertising • We have an impact on users • A user is seen more than 20 times a day in average • Every bid has an influence on our competitors • We want to provide a better online advertising experience • Personalized • Cross-device • Long tail (new users, new products)
  8. 8. Copyright © 2015 Criteo Machine learning challenges • Optimal bidding strategies under uncertainty -- reinforcement learning, policy learning • Probabilistic match of devices • Classification/prediction of time series • Long tail (users, products) -- transfer learning, factorization • Offline metrics – counterfactual analysis
  9. 9. Copyright © 2015 Criteo The good news • New generations of algorithms • NLP (word embeddings), reinforcement learning, policy learning, deep networks • Releases of ML infrastructures • Caffe on Spark, TensorFlow, Torch, PhotonML, GPUs inside clusters → strong traction in the academic/industrial community
  10. 10. Copyright © 2015 Criteo The good news (c’ed) • A lot of data is available • Interactions with banners : clicks • Interactions with products/advertisers : sales, baskets, home views, listings, visit history → faster decision-making in AB test, feature engineering of ML models • New data is coming : mobile, cross-device, (offline) → we need to make sense of it
  11. 11. Copyright © 2015 Criteo Conclusions • Machine learning applies well to online advertising at scale • Yet we still need to improve the users’ experience significantly • The community is pushing new algorithms and new infrastructures forward • Lots of new data is coming : we need to make sense of it
  12. 12. Copyright © 2015 Criteo Thanks! Questions? o.koch@criteo.com

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