  Matrix Factorization is one of the most successful recommender system
techniques over the past decade. However, the classic probabilistic theory
framework for matrix factorization is modeled using normal distributions. To
find better probabilistic models, algorithms such as RankMat, ZeroMat and
DotMat have been invented in recent years. In this paper, we model the user
rating behavior in recommender system as a Poisson process, and design an
algorithm that relies on no input data to solve the recommendation problem and
the cold start issue at the same time. We prove the superiority of our
algorithm in comparison with matrix factorization, random placement, Zipf
placement, ZeroMat, DotMat, etc.
