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

arXiv: https://arxiv.org/abs/2212.10460 · HTML: https://mlsanity.com/paper/2212.10460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Controlling Fairness and Bias in Dynamic Learning-to-Rank](https://mlsanity.com/paper/2005.14713/) (2020)
- [MatRec: Matrix Factorization for Highly Skewed Dataset](https://mlsanity.com/paper/2011.04395/) (2020)
- [Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction](https://mlsanity.com/paper/2106.07347/) (2021)
- [ZeroMat: Solving Cold-start Problem of Recommender System with No Input Data](https://mlsanity.com/paper/2112.03084/) (2021)
- [KL-Mat : Fair Recommender System via Information Geometry](https://mlsanity.com/paper/2204.13583/) (2022)
- [DotMat: Solving Cold-start Problem and Alleviating Sparsity Problem for Recommender Systems](https://mlsanity.com/paper/2206.00151/) (2022)
- [RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement](https://mlsanity.com/paper/2204.13016/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization](https://mlsanity.com/paper/2301.03791/) (2023)

## Abstract (arXiv)

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.