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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New machine learning challenges at Criteo
Olivier Koch
Engineering Program Manager, Criteo
Rythm Meetup
June 15, 2016

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Banners… what else?
2
Advertiser Publisher

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Machine learning applications at Criteo
• Bidding (2nd price auctions)
• Product recommendation
• Banner look and feel selection

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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

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Data sparsity
10 000 displays
lead to
50 clicks
lead to
1 sale

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Now what?

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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)

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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

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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

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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

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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

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Thanks! Questions?
o.koch@criteo.com