  Cold-start and sparsity problem are two key intrinsic problems to recommender
systems. During the past two decades, researchers and industrial practitioners
have spent considerable amount of efforts trying to solve the problems.
However, for cold-start problem, most research relies on importing side
information to transfer knowledge. A notable exception is ZeroMat, which uses
no extra input data. Sparsity is a lesser noticed problem. In this paper, we
propose a new algorithm named DotMat that relies on no extra input data, but is
capable of solving cold-start and sparsity problems. In experiments, we prove
that like ZeroMat, DotMat can achieve competitive results with recommender
systems with full data, such as the classic matrix factorization algorithm.
