PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data

TL;DR

PoissonMat introduces a novel Poisson-based matrix factorization approach that effectively addresses cold start problems without requiring input data, outperforming existing methods in recommender systems.

Contribution

It models user ratings as a Poisson process and proposes a new algorithm that solves cold start issues without input data, surpassing prior models.

Findings

Superiority over traditional matrix factorization methods

Effective cold start problem resolution without input data

Outperforms ZeroMat, DotMat, and other placement strategies

Abstract

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.