# RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement

arXiv: https://arxiv.org/abs/2204.13016 · HTML: https://mlsanity.com/paper/2204.13016/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2022.

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Matrix Factorization is a widely adopted technique in the field of recommender system. Matrix Factorization techniques range from SVD, LDA, pLSA, SVD++, MatRec, Zipf Matrix Factorization and Item2Vec. In recent years, distributed word embeddings have inspired innovation in the area of recommender systems. Word2vec and GloVe have been especially emphasized in many industrial application scenario such as Xiaomi's recommender system. In this paper, we propose a new matrix factorization inspired by the theory of power law and GloVe. Instead of the exponential nature of GloVe model, we take advantage of Pareto Distribution to model our loss function. Our method is explainable in theory and easy-to-implement in practice. In the experiment section, we prove our approach is superior to vanilla matrix factorization technique and comparable with GloVe-based model in both accuracy and fairness metrics.