  Privacy-preserving recommendations are recently gaining momentum, since the
decentralized user data is increasingly harder to collect, by recommendation
service providers, due to the serious concerns over user privacy and data
security. This situation is further exacerbated by the strict government
regulations such as Europe's General Data Privacy Regulations(GDPR). Federated
Learning(FL) is a newly developed privacy-preserving machine learning paradigm
to bridge data repositories without compromising data security and privacy.
Thus many federated recommendation(FedRec) algorithms have been proposed to
realize personalized privacy-preserving recommendations. However, existing
FedRec algorithms, mostly extended from traditional collaborative filtering(CF)
method, cannot address cold-start problem well. In addition, their performance
overhead w.r.t. model accuracy, trained in a federated setting, is often
non-negligible comparing to centralized recommendations. This paper studies
this issue and presents FL-MV-DSSM, a generic content-based federated
multi-view recommendation framework that not only addresses the cold-start
problem, but also significantly boosts the recommendation performance by
learning a federated model from multiple data source for capturing richer
user-level features. The new federated multi-view setting, proposed by
FL-MV-DSSM, opens new usage models and brings in new security challenges to FL
in recommendation scenarios. We prove the security guarantees of \xxx, and
empirical evaluations on FL-MV-DSSM and its variations with public datasets
demonstrate its effectiveness. Our codes will be released if this paper is
accepted.
