  Predicting the expected value or 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 aspects is
handling delayed feedback with respect to conversions, which can happen
multiple times with varying 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 based on
three core 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 the distribution.

Machine Learning