  Alleviating the delayed feedback problem is of crucial importance for the
conversion rate(CVR) prediction in online advertising. Previous delayed
feedback modeling methods using an observation window to balance the trade-off
between waiting for accurate labels and consuming fresh feedback. Moreover, to
estimate CVR upon the freshly observed but biased distribution with fake
negatives, the importance sampling is widely used to reduce the distribution
bias. While effective, we argue that previous approaches falsely treat fake
negative samples as real negative during the importance weighting and have not
fully utilized the observed positive samples, leading to suboptimal
performance.
  In this work, we propose a new method, DElayed Feedback modeling with
UnbiaSed Estimation, (DEFUSE), which aim to respectively correct the importance
weights of the immediate positive, the fake negative, the real negative, and
the delay positive samples at finer granularity. Specifically, we propose a
two-step optimization approach that first infers the probability of fake
negatives among observed negatives before applying importance sampling. To
fully exploit the ground-truth immediate positives from the observed
distribution, we further develop a bi-distribution modeling framework to
jointly model the unbiased immediate positives and the biased delay
conversions. Experimental results on both public and our industrial datasets
validate the superiority of DEFUSE. Codes are available at
https://github.com/ychen216/DEFUSE.git.
