  Online advertising has typically been more personalized than offline
advertising, through the use of machine learning models and real-time auctions
for ad targeting. One specific task, predicting the likelihood of conversion
(i.e.\ the probability a user will purchase the advertised product), is crucial
to the advertising ecosystem for both targeting and pricing ads. Currently,
these models are often trained by observing individual user behavior, but,
increasingly, regulatory and technical constraints are requiring
privacy-preserving approaches. For example, major platforms are moving to
restrict tracking individual user events across multiple applications, and
governments around the world have shown steadily more interest in regulating
the use of personal data. Instead of receiving data about individual user
behavior, advertisers may receive privacy-preserving feedback, such as the
number of installs of an advertised app that resulted from a group of users. In
this paper we outline the recent privacy-related changes in the online
advertising ecosystem from a machine learning perspective. We provide an
overview of the challenges and constraints when learning conversion models in
this setting. We introduce a novel approach for training these models that
makes use of post-ranking signals. We show using offline experiments on real
world data that it outperforms a model relying on opt-in data alone, and
significantly reduces model degradation when no individual labels are
available. Finally, we discuss future directions for research in this evolving
area.
