MLconf New York 15 April, 230 fifth Avenue NY 10001.
This talk, will briefly introduce the display advertising marketplace, its stakeholders and the key performance metrics. We will then present the models we have developed at Criteo for bidding in real-time auctions, product recommendation, and look & feel optimization at scale (1B+ monthly users, 3B+ products in our catalog, and 30K ad displayed / sec at peak traffic). For these tasks, we’ve moved over time from predicting rare, binary events (clicks) to predicting very rare events (sales) and continuous events (sales amounts), all of them being quite noisy, and we’ll discuss the different methods that we have tried to build these models (such as generalized linear models, trees or factorization machines). We’ll continue by discussing how we evaluate these models both offline and online. We will describe the infrastructure for large-scale distributed data processing that these algorithms rely upon and discuss different optimization techniques we have experimented with (such as SGD, L-BFGS, SVRG). Finally, we will conclude with future areas of research and discuss open challenges we are currently facing.”
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Damien Lefortier
Damien Lefortier is a Senior Machine Learning Engineer and Tech Lead in the Prediction Machine Learning team at Criteo where he has been actively involved in the development of Criteo’s large scale distributed machine learning library as well as in improving Criteo’s predictive algorithms for ad targeting. Before Criteo, Damien worked 3 years in the Search team at Yandex where he focused both on search quality and on infrastructure. At the same time, he started his PhD in information retrieval at the University of Amsterdam. His research work has been published at top tier conferences, such as WWW and CIKM.
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