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.

本报告提出了 DRIFT，这是一种基于 SAROS 的联邦推荐系统框架，使用隐式反馈来实现精细推荐，同时保护用户隐私，并证明了 DRIFT 在计算时间和安全性方面的优势。