# Towards Fair Recommendation in Two-Sided Platforms

arXiv: https://arxiv.org/abs/2201.01180 · HTML: https://mlsanity.com/paper/2201.01180/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 66 references that are in the map.
- First posted on arXiv 26 Dec 2021.

## Builds on (most cited first, 22 of 22)

- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search](https://mlsanity.com/paper/1905.01989/) (2019)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [Managing Popularity Bias in Recommender Systems with Personalized Re-ranking](https://mlsanity.com/paper/1901.07555/) (2019)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Translation-based Recommendation](https://mlsanity.com/paper/1707.02410/) (2017)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Estimating Position Bias without Intrusive Interventions](https://mlsanity.com/paper/1812.05161/) (2018)
- [Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness](https://mlsanity.com/paper/1907.13158/) (2019)
- [Fair Division Under Cardinality Constraints](https://mlsanity.com/paper/1804.09521/) (2018)
- [Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations](https://mlsanity.com/paper/1811.08690/) (2018)
- [Fair Allocation of Indivisible Goods: Improvement and Generalization](https://mlsanity.com/paper/1704.00222/) (2017)
- [Approximation Algorithms for Computing Maximin Share Allocations](https://mlsanity.com/paper/1503.00941/) (2015)
- [Approximation Algorithms for Maximin Fair Division](https://mlsanity.com/paper/1703.01851/) (2017)
- [Comparing Fair Ranking Metrics](https://mlsanity.com/paper/2009.01311/) (2020)
- [Comparing Approximate Relaxations of Envy-Freeness](https://mlsanity.com/paper/1806.03114/) (2018)
- [When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces](https://mlsanity.com/paper/2102.00141/) (2021)
- [Groupwise Maximin Fair Allocation of Indivisible Goods](https://mlsanity.com/paper/1711.07621/) (2017)
- [Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations](https://mlsanity.com/paper/1909.10005/) (2019)
- [Fair Division of a Graph](https://mlsanity.com/paper/1705.10239/) (2017)
- [Almost Envy-Free Allocations with Connected Bundles](https://mlsanity.com/paper/1808.09406/) (2018)
- [Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval](https://mlsanity.com/paper/2011.07359/) (2020)

## Built on by (most cited first, 8 of 8)

- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Learning with Exposure Constraints in Recommendation Systems](https://mlsanity.com/paper/2302.01377/) (2023)
- [Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations](https://mlsanity.com/paper/2210.07755/) (2022)
- [Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation](https://mlsanity.com/paper/2305.09330/) (2023)
- [DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model](https://mlsanity.com/paper/2410.02791/) (2024)
- [Measuring Individual User Fairness with User Similarity and Effectiveness Disparity](https://mlsanity.com/paper/2602.02516/) (2026)
- [Centralized Selection with Preferences in the Presence of Biases](https://mlsanity.com/paper/2409.04897/) (2024)
- [Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching](https://mlsanity.com/paper/2602.15752/) (2026)

## Abstract (arXiv)

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. On the other hand, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed em FairRec algorithm guarantees Maxi-Min Share (α-MMS) of exposure for the producers, and Envy-Free up to One Item (EF1) fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of em FairRec in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.