# Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems

arXiv: https://arxiv.org/abs/2212.10459 · HTML: https://mlsanity.com/paper/2212.10459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Controlling Fairness and Bias in Dynamic Learning-to-Rank](https://mlsanity.com/paper/2005.14713/) (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)
- [GAPfm: Optimal Top-N Recommendations for Graded Relevance Domains](https://mlsanity.com/paper/1307.3855/) (2013)
- [Extremal GloVe: Theoretically Accurate Distributed Word Embedding by Tail Inference](https://mlsanity.com/paper/2204.13009/) (2022)

## Abstract (arXiv)

Learning to rank is an effective recommendation approach since its introduction around 2010. Famous algorithms such as Bayesian Personalized Ranking and Collaborative Less is More Filtering have left deep impact in both academia and industry. However, most learning to rank approaches focus on improving technical accuracy metrics such as AUC, MRR and NDCG. Other evaluation metrics of recommender systems like fairness have been largely overlooked until in recent years. In this paper, we propose a new learning to rank algorithm named Pareto Pairwise Ranking. We are inspired by the idea of Bayesian Personalized Ranking and power law distribution. We show that our algorithm is competitive with other algorithms when evaluated on technical accuracy metrics. What is more important, in our experiment section we demonstrate that Pareto Pairwise Ranking is the most fair algorithm in comparison with 9 other contemporary algorithms.