# MatRec: Matrix Factorization for Highly Skewed Dataset

arXiv: https://arxiv.org/abs/2011.04395 · HTML: https://mlsanity.com/paper/2011.04395/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation](https://mlsanity.com/paper/2211.01328/) (2022)
- [Theoretically Accurate Regularization Technique for Matrix Factorization based Recommender Systems](https://mlsanity.com/paper/2205.10492/) (2022)
- [PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data](https://mlsanity.com/paper/2212.10460/) (2022)
- [Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization](https://mlsanity.com/paper/2301.03791/) (2023)

## Abstract (arXiv)

Recommender systems is one of the most successful AI technologies applied in the internet cooperations. Popular internet products such as TikTok, Amazon, and YouTube have all integrated recommender systems as their core product feature. Although recommender systems have received great success, it is well known for highly skewed datasets, engineers and researchers need to adjust their methods to tackle the specific problem to yield good results. Inability to deal with highly skewed dataset usually generates hard computational problems for big data clusters and unsatisfactory results for customers. In this paper, we propose a new algorithm solving the problem in the framework of matrix factorization. We model the data skewness factors in the theoretic modeling of the approach with easy to interpret and easy to implement formulas. We prove in experiments our method generates comparably favorite results with popular recommender system algorithms such as Learning to Rank , Alternating Least Squares and Deep Matrix Factorization.