  Matrix factorization is one of the most commonly used technologies in
recommendation system. With the promotion of recommendation system in
e-commerce shopping, online video and other aspects, distributed recommendation
system has been widely promoted, and the privacy problem of multi-source data
becomes more and more important. Based on Federated learning technology, this
paper proposes a shared matrix factorization scheme called SharedMF. Firstly, a
distributed recommendation system is built, and then secret sharing technology
is used to protect the privacy of local data. Experimental results show that
compared with the existing homomorphic encryption methods, our method can have
faster execution speed without privacy disclosure, and can better adapt to
recommendation scenarios with large amount of data.
