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.

翻译：个性化推荐是当今互联网生态的重要组成部分，帮助艺术家和创作者触达感兴趣的用户，同时助力用户发现新颖且吸引人的内容。然而，许多用户目前对实施个性化推荐的平台持怀疑态度，部分原因是历史上对个人数据和数据隐私的粗放处理。如今，依赖个性化推荐的企业正进入一个全新范式，其诸多系统必须进行彻底改造，以将隐私保护置于首位。本文提出一种面向个性化推荐的算法，该算法可同时实现精确测量与差分隐私。我们以广告作为示例应用场景，通过离线实验量化所提出的隐私保护算法与两类极端方案（即隐私保护的、非个性化的实现方案，以及非隐私保护的、个性化的实现方案）相比，在用户体验、广告主价值及平台收入等关键指标上的影响差异。