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：个性化推荐构成了当今互联网生态系统的重要组成部分，既能帮助艺术家和创作者触达感兴趣的用户，也能帮助用户发现新颖且引人入胜的内容。然而，由于历史上对个人数据及数据隐私的轻率处理，如今许多用户对实施个性化推荐的平台持怀疑态度。当下，依赖个性化推荐的企业正进入一个全新范式，其大量系统必须进行根本性重构以遵循隐私优先原则。本文提出一种既支持精确测量又具备差分隐私保障的个性化推荐算法。我们以广告作为示例应用场景，通过离线实验量化该隐私保护算法相较于（隐私保护下的）非个性化实现与非隐私保护的个性化实现这两个极端方案，在用户体验、广告主价值及平台收入等关键指标上的影响。