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Estimating Conversion Rate in Display Advertising from Past Erformance Data by Kuang-chih Lee; Burkay Orten; Ali Dasdan; Wentong Li is a scholarly article available to read on EtoBox.

What is Estimating Conversion Rate in Display Advertising from Past Erformance Data about?

In targeted display advertising, the goal is to identify the best opportunities to display a banner ad to an online user who is most likely to take a desired action such as purchasing a product or signing up for a newsletter. Finding the best ad impression, i.e., the opportunity to show an ad to a user, requires the ability to estimate the probability that the user who sees the ad on his or her browser will take an action, i.e., the user will convert. However, conversion probability estimation is a challenging task since there is extreme data sparsity across different data dimensions and the conversion event occurs rarely. In this paper, we present our approach to conversion rate estimation which relies on utilizing past performance observations along user, publisher and advertiser data hierarchies. More specifically, we model the conversion event at different select hierarchical levels with separate binomial distributions and estimate the distribution parameters individually. Then we demonstrate how we can combine these individual estimators using logistic regression to accurately identify conversion events. In our presentation, we also discuss main practical considerations such a

Author

Kuang-chih Lee; Burkay Orten; Ali Dasdan; Wentong Li

Publisher

ACM

Published

2012

Language

EN