Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

New machine learning challenges at Criteo

837 Aufrufe

Veröffentlicht am

A brief overview of the machine learning challenges at Criteo

Veröffentlicht in: Technologie
  • Als Erste(r) kommentieren

  • Gehören Sie zu den Ersten, denen das gefällt!

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

×