  One of the challenges in display advertising is that the distribution of
features and click through rate (CTR) can exhibit large shifts over time due to
seasonality, changes to ad campaigns and other factors. The predominant
strategy to keep up with these shifts is to train predictive models
continuously, on fresh data, in order to prevent them from becoming stale.
However, in many ad systems positive labels are only observed after a possibly
long and random delay. These delayed labels pose a challenge to data freshness
in continuous training: fresh data may not have complete label information at
the time they are ingested by the training algorithm. Naive strategies which
consider any data point a negative example until a positive label becomes
available tend to underestimate CTR, resulting in inferior user experience and
suboptimal performance for advertisers. The focus of this paper is to identify
the best combination of loss functions and models that enable large-scale
learning from a continuous stream of data in the presence of delayed labels. In
this work, we compare 5 different loss functions, 3 of them applied to this
problem for the first time. We benchmark their performance in offline settings
on both public and proprietary datasets in conjunction with shallow and deep
model architectures. We also discuss the engineering cost associated with
implementing each loss function in a production environment. Finally, we
carried out online experiments with the top performing methods, in order to
validate their performance in a continuous training scheme. While training on
668 million in-house data points offline, our proposed methods outperform
previous state-of-the-art by 3% relative cross entropy (RCE). During online
experiments, we observed 55% gain in revenue per thousand requests (RPMq)
against naive log loss.
