# DeepFair: Deep Learning for Improving Fairness in Recommender Systems

arXiv: https://arxiv.org/abs/2006.05255 · HTML: https://mlsanity.com/paper/2006.05255/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers](https://mlsanity.com/paper/2104.09024/) (2021)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System](https://mlsanity.com/paper/2507.02000/) (2025)
- [Mitigating Algorithmic Bias with Limited Annotations](https://mlsanity.com/paper/2207.10018/) (2022)
- [Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation](https://mlsanity.com/paper/2305.09330/) (2023)
- [Deep Context Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2308.06037/) (2023)
- [Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks](https://mlsanity.com/paper/2303.01297/) (2023)
- [Deep Variational Models for Collaborative Filtering-based Recommender Systems](https://mlsanity.com/paper/2107.12677/) (2021)
- [Towards Fairness in Provably Communication-Efficient Federated Recommender Systems](https://mlsanity.com/paper/2405.15788/) (2024)
- [From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review](https://mlsanity.com/paper/2602.22438/) (2026)
- [Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation](https://mlsanity.com/paper/2603.11936/) (2026)

## Abstract (arXiv)

The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users. Experimental results show that it is possible to make fair recommendations without losing a significant proportion of accuracy.