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系统的重要组成部分，它能帮助艺术家和创作者触达感兴趣的用户，同时帮助用户发现新颖且吸引人的内容。然而，当前许多用户对提供个性化推荐的平台持怀疑态度，部分原因在于过去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新的范式，其许多系统必须彻底改造，以将隐私置于首位。本文提出一种个性化推荐算法，同时支持精确测量和差分隐私保护。我们以广告作为示例应用，通过离线实验量化所提隐私保护算法对用户体验、广告主价值及平台收入等关键指标的影响，并将其与（隐私保护的）非个性化推荐和（非隐私保护的）个性化推荐这两种极端实现进行对比。