# FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning

arXiv: https://arxiv.org/abs/2006.16742 · HTML: https://mlsanity.com/paper/2006.16742/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 30 of 44)

- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [UP5: Unbiased Foundation Model for Fairness-aware Recommendation](https://mlsanity.com/paper/2305.12090/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [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)
- [Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency](https://mlsanity.com/paper/2401.10545/) (2024)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Ensuring User-side Fairness in Dynamic Recommender Systems](https://mlsanity.com/paper/2308.15651/) (2023)
- [FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning](https://mlsanity.com/paper/2206.03200/) (2022)
- [Joint Multisided Exposure Fairness for Recommendation](https://mlsanity.com/paper/2205.00048/) (2022)
- [Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey](https://mlsanity.com/paper/2211.05617/) (2022)
- [Unlearning Protected User Attributes in Recommendations with Adversarial Training](https://mlsanity.com/paper/2206.04500/) (2022)
- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Towards Equity and Algorithmic Fairness in Student Grade Prediction](https://mlsanity.com/paper/2105.06604/) (2021)
- [The Pursuit of Fairness in Artificial Intelligence Models: A Survey](https://mlsanity.com/paper/2403.17333/) (2024)
- [Why Do We Click: Visual Impression-aware News Recommendation](https://mlsanity.com/paper/2109.12651/) (2021)
- [Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation](https://mlsanity.com/paper/2209.09592/) (2022)
- [DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision](https://mlsanity.com/paper/2303.08403/) (2023)
- [Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning](https://mlsanity.com/paper/2306.03293/) (2023)
- [Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder](https://mlsanity.com/paper/2204.00536/) (2022)
- [FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data](https://mlsanity.com/paper/2212.06750/) (2022)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search](https://mlsanity.com/paper/2412.20086/) (2024)
- [Taxonomy of the Retrieval System Framework: Pitfalls and Paradigms](https://mlsanity.com/paper/2601.20131/) (2026)
- [FairRec: Fairness Testing for Deep Recommender Systems](https://mlsanity.com/paper/2304.07030/) (2023)
- [Interpolating Item and User Fairness in Multi-Sided Recommendations](https://mlsanity.com/paper/2306.10050/) (2023)
- [FairSTG: Countering performance heterogeneity via collaborative sample-level optimization](https://mlsanity.com/paper/2403.12391/) (2024)
- [Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness](https://mlsanity.com/paper/2502.17921/) (2025)
- [Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation](https://mlsanity.com/paper/2305.09330/) (2023)

## Abstract (arXiv)

News recommendation is important for online news services. Existing news recommendation models are usually learned from users' news click behaviors. Usually the behaviors of users with the same sensitive attributes (e.g., genders) have similar patterns and news recommendation models can easily capture these patterns. It may lead to some biases related to sensitive user attributes in the recommendation results, e.g., always recommending sports news to male users, which is unfair since users may not receive diverse news information. In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes. In our approach, we propose to decompose the user interest model into two components. One component aims to learn a bias-aware user embedding that captures the bias information on sensitive user attributes, and the other aims to learn a bias-free user embedding that only encodes attribute-independent user interest information for fairness-aware news recommendation. In addition, we propose to apply an attribute prediction task to the bias-aware user embedding to enhance its ability on bias modeling, and we apply adversarial learning to the bias-free user embedding to remove the bias information from it. Moreover, we propose an orthogonality regularization method to encourage the bias-free user embeddings to be orthogonal to the bias-aware one to better distinguish the bias-free user embedding from the bias-aware one. For fairness-aware news ranking, we only use the bias-free user embedding. Extensive experiments on benchmark dataset show that our approach can effectively improve fairness in news recommendation with minor performance loss.