Nowadays there are more and more items available online, this makes it hard for users to find items that they like. Recommender systems aim to find the item who best suits the user, using his historical interactions. Depending on the context, these interactions may be more or less sensitive and collecting them brings an important problem concerning the users' privacy. Federated systems have shown that it is possible to make accurate and efficient recommendations without storing users' personal information. However, these systems use instantaneous feedback from the user. In this report, we propose DRIFT, a federated architecture for recommender systems, using implicit feedback. Our learning model is based on a recent algorithm for recommendation with implicit feedbacks SAROS. We aim to make recommendations as precise as SAROS, without compromising the users' privacy. In this report we show that thanks to our experiments, but also thanks to a theoretical analysis on the convergence. We have shown also that the computation time has a linear complexity with respect to the number of interactions made. Finally, we have shown that our algorithm is secure, and participants in our federated system cannot guess the interactions made by the user, except DOs that have the item involved in the interaction.

翻译：如今，在线可用的物品日益增多，这使得用户难以找到自己喜爱的物品。推荐系统旨在利用用户的历史交互记录，找到最适合用户的物品。根据具体情境，这些交互可能具有不同程度的敏感性，收集这些数据会带来严重的用户隐私问题。联邦系统已证明，在不存储用户个人信息的情况下，实现准确高效的推荐是可能的。然而，这些系统通常使用用户的即时反馈。在本报告中，我们提出了DRIFT，一种用于推荐系统的联邦架构，该架构使用隐式反馈。我们的学习模型基于一种最新的用于隐式反馈推荐的算法SAROS。我们的目标是实现与SAROS同等精度的推荐，同时不损害用户的隐私。在本报告中，我们通过实验以及收敛性的理论分析证明了算法的有效性。我们还证明，计算时间与交互次数呈线性复杂度。最后，我们证明所提算法是安全的，联邦系统中的参与者无法推测用户的具体交互行为，除非是涉及交互物品的权威机构（DOs）。