An Analysis of Selection Bias Issue for Online Advertising
- URL: http://arxiv.org/abs/2206.03853v1
- Date: Tue, 7 Jun 2022 12:29:40 GMT
- Title: An Analysis of Selection Bias Issue for Online Advertising
- Authors: Shinya Suzumura and Hitoshi Abe
- Abstract summary: We show a selection bias issue that is present in an auction system.
We analyze that the selection bias destroy truthfulness of the auction.
Experiment shows that the selection bias is drastically reduced by using a multi-task learning which learns the data for all advertisements.
- Score: 0.30458514384586394
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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