Large-scale Validation of Counterfactual Learning Methods: A Test-Bed

Understand

The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news.

• Recent approaches for off-policy evaluation and learning in these settings appear promising.
• With this paper, we provide real-world data and a standardized test-bed to systematically investigate these algorithms using data from display advertising.
• In particular, we consider the problem of filling a banner ad with an aggregate of multiple products the user may want to purchase.