  In display advertising, predicting the conversion rate (CVR), meaning the
probability that a user takes a predefined action on an advertiser's website,
is a fundamental task for estimating the value of displaying an advertisement
to a user. There are two main challenges in CVR prediction due to delayed
feedback. First, some positive labels are not correctly observed in training
data because some conversions do not occur immediately after a click. Second,
delay mechanisms are not uniform among instances, meaning some positive
feedback are much more frequently observed than others. It is widely
acknowledged that these problems lead to severe bias in CVR prediction. To
overcome these challenges, we propose two unbiased estimators: one for CVR
prediction and the other for bias estimation. Subsequently, we propose a dual
learning algorithm in which a CVR predictor and a bias estimator are trained in
alternating fashion using only observable conversions. The proposed algorithm
is the first of its kind to address the two major challenges in a theoretically
sophisticated manner. Empirical evaluations using synthetic datasets
demonstrate the practical value of the proposed approach.
