new machine learning challenges at criteo

Copyright © 2015 Criteo

New machine learning challenges at Criteo

Olivier Koch

Engineering Program Manager, Criteo

Rythm Meetup

June 15, 2016


Banners… what else?

2

Advertiser Publisher


Machine learning applications at Criteo

• Bidding (2nd price auctions)

• Product recommendation

• Banner look and feel selection


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


Data sparsity

10 000 displays

lead to

50 clicks

lead to

1 sale


Now what?


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)


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


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


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


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


Thanks! Questions?

[email protected]

new machine learning challenges at criteo

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