# DotMat: Solving Cold-start Problem and Alleviating Sparsity Problem for Recommender Systems

arXiv: https://arxiv.org/abs/2206.00151 · HTML: https://mlsanity.com/paper/2206.00151/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2022.

## Built on by (most cited first, 3 of 3)

- [PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data](https://mlsanity.com/paper/2212.10460/) (2022)
- [Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems](https://mlsanity.com/paper/2212.10459/) (2022)
- [Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization](https://mlsanity.com/paper/2301.03791/) (2023)

## Abstract (arXiv)

Cold-start and sparsity problem are two key intrinsic problems to recommender systems. During the past two decades, researchers and industrial practitioners have spent considerable amount of efforts trying to solve the problems. However, for cold-start problem, most research relies on importing side information to transfer knowledge. A notable exception is ZeroMat, which uses no extra input data. Sparsity is a lesser noticed problem. In this paper, we propose a new algorithm named DotMat that relies on no extra input data, but is capable of solving cold-start and sparsity problems. In experiments, we prove that like ZeroMat, DotMat can achieve competitive results with recommender systems with full data, such as the classic matrix factorization algorithm.