Title: Learning Theory and Algorithms for Revenue Management in Sponsored Search,
Abstract: 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.