# Multisided Fairness for Recommendation

arXiv: https://arxiv.org/abs/1707.00093 · HTML: https://mlsanity.com/paper/1707.00093/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2017.

## Built on by (most cited first, 30 of 70)

- [User-oriented Fairness in Recommendation](https://mlsanity.com/paper/2104.10671/) (2021)
- [Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features](https://mlsanity.com/paper/2104.14537/) (2021)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)
- [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)
- [CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems](https://mlsanity.com/paper/2204.08085/) (2022)
- [Two-sided fairness in rankings via Lorenz dominance](https://mlsanity.com/paper/2110.15781/) (2021)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Online certification of preference-based fairness for personalized recommender systems](https://mlsanity.com/paper/2104.14527/) (2021)
- [Joint Multisided Exposure Fairness for Recommendation](https://mlsanity.com/paper/2205.00048/) (2022)
- [The Impossibility of Fair LLMs](https://mlsanity.com/paper/2406.03198/) (2024)
- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [Achieving Counterfactual Fairness for Causal Bandit](https://mlsanity.com/paper/2109.10458/) (2021)
- [Estimation of Fair Ranking Metrics with Incomplete Judgments](https://mlsanity.com/paper/2108.05152/) (2021)
- [Optimizing generalized Gini indices for fairness in rankings](https://mlsanity.com/paper/2204.06521/) (2022)
- [T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems](https://mlsanity.com/paper/2107.08959/) (2021)
- [Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness](https://mlsanity.com/paper/2110.08353/) (2021)
- [Performative Recommendation: Diversifying Content via Strategic Incentives](https://mlsanity.com/paper/2302.04336/) (2023)
- [Fairness and Transparency in Recommendation: The Users' Perspective](https://mlsanity.com/paper/2103.08786/) (2021)
- [The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation](https://mlsanity.com/paper/2202.13307/) (2022)
- [Dynamic fairness-aware recommendation through multi-agent social choice](https://mlsanity.com/paper/2303.00968/) (2023)
- [Fairness Through Regularization for Learning to Rank](https://mlsanity.com/paper/2102.05996/) (2021)
- [Towards Fair Recommendation in Two-Sided Platforms](https://mlsanity.com/paper/2201.01180/) (2021)
- [Learning with Exposure Constraints in Recommendation Systems](https://mlsanity.com/paper/2302.01377/) (2023)
- [FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness](https://mlsanity.com/paper/2504.07801/) (2025)
- [Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists](https://mlsanity.com/paper/2404.04269/) (2024)
- [DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision](https://mlsanity.com/paper/2303.08403/) (2023)
- [Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting](https://mlsanity.com/paper/2508.20401/) (2025)

## Abstract (arXiv)

Recent work on machine learning has begun to consider issues of fairness. In this paper, we extend the concept of fairness to recommendation. In particular, we show that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered. Based on these considerations, we present a taxonomy of classes of fairness-aware recommender systems and suggest possible fairness-aware recommendation architectures.