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Handling many conversions per click in modeling delayed feedback

Andrew Evdokimov
Vinodh Krishnan
Pan Li
Wynn Vonnegut
Jayden Wang
ADKDD 2021 (2021)


Predicting the expected number of post-click conversions (purchases or other events) is a key task in performance-based digital advertising. In training a conversion optimizer model, one of the most crucial aspect is handling the delayed feedback with respect to conversions, which can happen multiple times with various delay. This task is difficult, as the delay distribution is different for each advertiser, is long-tailed, often does not follow any particular class of parametric distributions, and can change over time. We tackle these challenges using an unbiased estimation model with the following three ideas. The first idea is to split the label as a sum of labels with different delay buckets, each of which trains only on mature label, the second is to use thermometer encoding to increase accuracy and reduce inference cost, and the third is to use auxiliary information to increase the stability of the model and to handle drift in distribution.

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