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.

翻译：矩阵保理是过去十年中最成功的推荐系统技术之一。 但是, 典型的矩阵保理概率理论框架是使用正常分布模型模型构建的。 为了找到更好的概率模型, 最近几年里发明了RankMat、 ZeroMat和DotMat等算法。 在本文中, 我们将推荐系统中的用户评级行为作为 Poisson 程序模型, 并设计一种不依赖输入数据解决推荐问题和冷点启动问题的算法。 我们证明了我们算法与矩阵保理学、 随机布置、 Zipf 放置、 ZeroMat、 DotMat 等相比的优越性 。