# Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation

arXiv: https://arxiv.org/abs/2509.09037 · HTML: https://mlsanity.com/paper/2509.09037/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Online certification of preference-based fairness for personalized recommender systems](https://mlsanity.com/paper/2104.14527/) (2021)
- [Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking](https://mlsanity.com/paper/2206.07247/) (2022)
- [Who Pays? Personalization, Bossiness and the Cost of Fairness](https://mlsanity.com/paper/2209.04043/) (2022)
- [The Constrained Round Robin Algorithm for Fair and Efficient Allocation](https://mlsanity.com/paper/1908.00161/) (2019)

## Built on by (most cited first, 1 of 1)

- [Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders](https://mlsanity.com/paper/2608.08284/) (2026)

## Abstract (arXiv)

Envy-freeness and the relaxation to Envy-freeness up to one item (EF-1) have been used as fairness concepts in the economics, game theory, and social choice literatures since the 1960s, and have recently gained popularity within the recommendation systems communities. In this short position paper we will give an overview of envy-freeness and its use in economics and recommendation systems; and illustrate why envy is not appropriate to measure fairness for use in settings where personalization plays a role.