PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data
This addresses the cold start problem for recommender systems, offering a novel approach that is incremental in improving probabilistic modeling.
The paper tackles the problem of cold start in recommender systems by modeling user rating behavior as a Poisson process, resulting in an algorithm that outperforms existing methods like matrix factorization and ZeroMat without requiring input data.
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.