  Personalized recommendations form an important part of today's internet
ecosystem, helping artists and creators to reach interested users, and helping
users to discover new and engaging content. However, many users today are
skeptical of platforms that personalize recommendations, in part due to
historically careless treatment of personal data and data privacy. Now,
businesses that rely on personalized recommendations are entering a new
paradigm, where many of their systems must be overhauled to be privacy-first.
In this article, we propose an algorithm for personalized recommendations that
facilitates both precise and differentially-private measurement. We consider
advertising as an example application, and conduct offline experiments to
quantify how the proposed privacy-preserving algorithm affects key metrics
related to user experience, advertiser value, and platform revenue compared to
the extremes of both (private) non-personalized and non-private, personalized
implementations.
