Position Auctions with Externalities and Brand Effects

TL;DR

This paper develops models for position auctions that incorporate externalities and brand effects, proposing axioms and algorithms to optimize ad placement and revenue.

Contribution

It introduces a general axiomatic framework for modeling click probabilities considering externalities and brand effects, with algorithms for optimal ad allocation.

Findings

Using axioms increases revenue when higher quality ads are ranked higher.

Algorithms for ad placement under these models are proposed.

Greedy ranking algorithms can cost up to half of the maximum social welfare.

Abstract

This paper presents models for predicted click-through rates in position auctions that take into account two possibilities that are not normally considered---that the identities of ads shown in other positions may affect the probability that an ad in a particular position receives a click (externalities) and that some ads may be less adversely affected by being shown in a lower position than others (brand effects). 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 either the models of externalities or brand effects that we consider. Finally, we analyze the performance of a greedy algorithm of ranking the ads by their expected cost-per-1000-impressions bids when the true click-through rates are governed by our model of predicted click-through rates with brand effects and illustrate that such an algorithm will potentially cost as much as half of the total possible social welfare.