论文标题
PoissonMat：使用泊松分布进行重塑矩阵分解并解决冷启动问题而没有输入数据
PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data
论文作者
论文摘要
矩阵分解是过去十年中最成功的推荐系统技术之一。但是，使用正常分布对基质分解的经典概率理论框架进行建模。为了找到更好的概率模型，近年来已经发明了诸如rankmat，Zeromat和dotmat之类的算法。在本文中，我们将推荐系统中的用户评级行为建模为泊松过程，并设计一种算法，该算法依赖于未输入数据来解决建议问题和冷启动问题。与基质分解，随机放置，ZIPF放置，Zeromat，Dotmat等相比，我们证明了算法的优势。
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.
