mlsanityData to 14 Oct 2026

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

arXiv 2509.09037 ↗First posted 10 Sep 2025cs.IR cs.AIcited by 1

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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.

Key facts. Cited by 1 paper 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

4 of 16 references.

References · 4

  1. 2021Online certification of preference-based fairness for personalized recommender systemscited by 22
  2. 2022Fair Ranking as Fair Division: Impact-Based Individual Fairness in Rankingcited by 13
  3. 2022Who Pays? Personalization, Bossiness and the Cost of Fairnesscited by 2
  4. 2019The Constrained Round Robin Algorithm for Fair and Efficient Allocationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenderscited by 0