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：现今网络上有越来越多的商品，这使得用户很难找到自己喜欢的商品。推荐系统旨在通过用户的历史交互来找到最适合他的商品。然而，这些交互可能会涉及到用户隐私，因此如何收集数据是一个重要问题。联邦学习已经展示了可以达到良好的推荐结果，同时不需要存储用户的个人信息。然而，这些系统通常使用即时反馈，忽略了隐式反馈。为了解决这个问题，我们提出了DRIFT，一种基于隐式反馈的联邦推荐系统。我们的学习模型基于最近的具有隐式反馈的推荐算法SAROS。我们的目标是在不影响用户隐私的情况下，实现与SAROS相同的精确推荐。通过实验和对收敛性的理论分析，我们表明了我们的算法的推荐效果与SAROS相当。我们还表明，我们的算法的计算时间与用户的交互次数呈线性关系。最后，我们还表明我们的算法是安全的，参与我们联邦系统的参与者除了拥有涉及商品的数据外，无法猜测用户的交互行为。