# ZeroMat: Solving Cold-start Problem of Recommender System with No Input Data

arXiv: https://arxiv.org/abs/2112.03084 · HTML: https://mlsanity.com/paper/2112.03084/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2021.

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

- [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)
- [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)

Recommender system is an applicable technique in most E-commerce commercial product technical designs. However, nearly all recommender system faces a challenge called the cold-start problem. The problem is so notorious that almost every industrial practitioner needs to resolve this issue when building recommender systems. Most cold-start problem solvers need some kind of data input as the starter of the system. On the other hand, many real-world applications place popular items or random items as recommendation results. In this paper, we propose a new technique called ZeroMat that requries no input data at all and predicts the user item rating data that is competitive in Mean Absolute Error and fairness metric compared with the classic matrix factorization with affluent data, and much better performance than random placement.