Learning Theory and Algorithms for Revenue Management in Sponsored Search
This addresses a key problem for online advertising platforms by improving revenue optimization, though it is incremental as it builds on existing auction and ranking methods.
The paper tackled the disconnect between click-through rate (CTR) optimization and auction performance in sponsored search by proposing loss functions based on auction metrics like AUC^R and SAUC to directly maximize revenue per mille (RPM). It introduced explicit and implicit ranking functions, and experiments on real-world datasets showed that the methods outperformed state-of-the-art approaches in platform revenue.
Online advertisement is the main source of revenue for Internet business. Advertisers are typically ranked according to a score that takes into account their bids and potential click-through rates(eCTR). Generally, the likelihood that a user clicks on an ad is often modeled by optimizing for the click through rates rather than the performance of the auction in which the click through rates will be used. This paper attempts to eliminate this dis-connection by proposing loss functions for click modeling that are based on final auction performance.In this paper, we address two feasible metrics (AUC^R and SAUC) to evaluate the on-line RPM (revenue per mille) directly rather than the CTR. And then, we design an explicit ranking function by incorporating the calibration fac-tor and price-squashed factor to maximize the revenue. Given the power of deep networks, we also explore an implicit optimal ranking function with deep model. Lastly, various experiments with two real world datasets are presented. In particular, our proposed methods perform better than the state-of-the-art methods with regard to the revenue of the platform.