A Lightweight Matrix Factorization for Recommendation With Local Differential Privacy in Big Data

The proliferation of various items recommended by Internet-based systems has resulted in the exponential growth of the number of ratings in the big data era. Recent advances in matrix factorization have made it an effective way to process these ratings for recommendations. However, we confront a challenge in deploying a matrix factorization model for recommendations in big data that arises from the typically resource-constrained local devices regarding their storage space and computing capacity. This paper proposes a novel lightweight matrix factorization for recommendations. Our scheme deploys shard grandients training on user local internet of things(IoT) devices, which makes it possible for users to train big data model locally. We design a two-phase solution to protect the security of users' data and reduce the dimension of the items. Moreover, we optimize the proposed scheme by introducing a stabilization mechanism to decrease the scale of the perturbed gradients. Some experimental results are given with two real online datasets, MovieLens and LibimSeTi. The theoretical analysis and experimental results demonstrate that compared with other methods, the proposed scheme achieves a good performance in terms of security, accuracy, and efficiency.