An Analysis of Selection Bias Issue for Online Advertising

TL;DR

This paper investigates how selection bias in online ad auctions undermines truthfulness and advertiser profits, and demonstrates that multi-task learning can significantly reduce this bias in practical auction systems.

Contribution

It combines theoretical analysis of selection bias with empirical evaluation, proposing multi-task learning as an effective mitigation method in online advertising auctions.

Findings

Selection bias destroys auction truthfulness and profits.

Multi-task learning significantly reduces selection bias.

Empirical results confirm the effectiveness of the proposed approach.

Abstract

In online advertising, a set of potential advertisements can be ranked by a certain auction system where usually the top-1 advertisement would be selected and displayed at an advertising space. In this paper, we show a selection bias issue that is present in an auction system. We analyze that the selection bias destroy truthfulness of the auction, which implies that the buyers (advertisers) on the auction can not maximize their profits. Although selection bias is well known in the field of statistics and there are lot of studies for it, our main contribution is to combine the theoretical analysis of the bias with the auction mechanism. In our experiment using online A/B testing, we evaluate the selection bias on an auction system whose ranking score is the function of predicted CTR (click through rate) of advertisement. The experiment showed that the selection bias is drastically reduced by using a multi-task learning which learns the data for all advertisements.