Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions

Understand

One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions.

• An unaddressed problem in previous approaches is the existence of highly non-uniform misprediction costs.
• While for model evaluation these costs have been taken into account through recently proposed business-aware offline metrics -- such as the Utility metric which measures the impact on advertiser profit -- this is not the case when training the models themselves.
• In this paper, to bridge the gap, we formally analyze the relationship between optimizing the Utility metric and the log loss, which is considered as one of the state-of-the-art approaches in conversion modeling.