Nowadays, as the number of items is increasing and that users have access to limited, user-item preference matrices in recommendation systems are always sparse. This leads a data sparsity problem. The latent factor analysis (LFA) model has been proposed solution As basis LFA model, singular value decomposition (SVD) especially biased SVD great effects high-dimensional sparse (HiDs) matrices. Ho...