Offline Evaluation of Response Prediction in Online Advertising Auctions

Abstract

Click-through rates and conversion rates are two core ma-chine learning problems in online advertising. The evalua-tion of such systems is often based on traditional supervised learning metrics that ignore how the predictions are used. These predictions are in fact part of bidding systems in on-line advertising auctions. We present here an empirical eval-uation of a metric that is specifically tailored for auctions in online advertising and show that it correlates better than standard metrics with A/B test results