AI

Position Auctions with Externalities

Abstract

This paper presents models for predicted click-through rates in position auctions that take into account the externalities ads shown in other positions may impose on the probability that an ad in a particular position receives a click. We present a general axiomatic methodology for how click probabilities are affected by the qualities of the ads in the other positions, and illustrate that using these axioms will increase revenue as long as higher quality ads tend to be ranked ahead of lower quality ads. We also present appropriate algorithms for selecting the optimal allocation of ads when predicted click-through rates are governed by a natural special case of this axiomatic model of externalities.