New machine learning challenges at Criteo
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This document summarizes machine learning challenges at Criteo, an online advertising company. It discusses how Criteo uses machine learning for bidding, product recommendations, and banner selection. It also outlines some of Criteo's machine learning challenges, including optimal bidding strategies under uncertainty, probabilistic cross-device matching, and modeling long tail users and products. The document concludes that while machine learning applies well to online advertising at scale, there is still room to improve the user experience through new algorithms and making sense of new data sources.
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Criteo is a performance advertising company that buys ad inventory and sells clicks at scale. They use real-time personalized product recommendations to select which ads to display to each user from billions of products. Their recommendation system retrieves candidate products for each user based on their browsing history and scores products from multiple data sources to select the top recommendations within 8 milliseconds to support their high traffic levels across many servers and data centers globally. They discuss challenges maintaining large user profiles, improving product data, and optimizing response time and independence of recommendations.
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- Describing the major players in programmatic advertising like DSPs, SSPs, DMPs, ad exchanges, and trading desks.
- Explaining the basic process of how programmatic advertising works from selecting platforms and vendors, to building strategic plans, implementing campaigns, and optimizing performance.
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1) Criteo is an advertising technology company that uses machine learning to target personalized ads to users across devices.
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- 1. Copyright © 2015 Criteo New machine learning challenges at Criteo Olivier Koch Engineering Program Manager, Criteo Rythm Meetup June 15, 2016
- 2. Copyright © 2015 Criteo Banners… what else? 2 Advertiser Publisher
- 3. Copyright © 2015 Criteo Machine learning applications at Criteo • Bidding (2nd price auctions) • Product recommendation • Banner look and feel selection
- 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. Copyright © 2015 Criteo Data sparsity 10 000 displays lead to 50 clicks lead to 1 sale
- 6. Copyright © 2015 Criteo Now what?
- 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. 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. 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. 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. 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. Copyright © 2015 Criteo Thanks! Questions? o.koch@criteo.com