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

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.

References in corpus (5)

• Controlling Fairness and Bias in Dynamic Learning-to-Rank
• ZeroMat: Solving Cold-start Problem of Recommender System with No Input Data
• MatRec: Matrix Factorization for Highly Skewed Dataset
• KL-Mat : Fair Recommender System via Information Geometry
• RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement

Cited by in corpus (5)

• Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems
• The Fallacy of Borda Count Method -- Why it is Useless with Group Intelligence and Shouldn't be Used with Big Data including Banking Customer Services
• Mitigating Position Bias with Regularization for Recommender Systems
• Zeroshot Listwise Learning to Rank Algorithm for Recommendation
• LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models