Machine Learning for Display Advertising @ Scale: In this talk, we 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.”
Similar to Damien Lefortier, Senior Machine Learning Engineer and Tech Lead in the Prediction Machine Learning team, Criteo at MLconf NYC - 4/15/16 (20)
12. We train our prediction models on our historical displays
Historical displays
Variables
Level of engagement of the user
Quality of inventory
User fatigue
For travel: time to check-in and number
of nights
: clicked displays : converted displays (size = order value)
Our ability to predict relies
greatly on the relevance of
the variables we consider
Machine Learning
Algorithms
15. Bob saw orange shoes
Some candidate products
Historical
Similar
Complementary
Most viewed
16. Products delivering the best performance are displayed
Variables
Products seen by the user
Time since product event
Level of similarity
Product features
Historical displays
: clicked products : converted products (size = order value)
Products are selected based
on their CTR, CR or OV
Machine Learning
Algorithms
18. Historical displays (color = look & feel)
We train our prediction models on our historical displays
Variables
Some of which we control:
How user interacts with banner
Organization of information
Colorset
Some of which we don’t:
Zone format
Publisher
: clicked displays : converted displays (size = order value)
Look and feel will be selected
based on its CTR, CR or OV
My company
BUY! BUY! BUY!
BUY!
Machine Learning
Algorithms