Recommend Me First: How the Relative Ordering of Recommendations Can Lead to Unfair Outcomes for Content Creators

Salima Jaoua, Nicolò Pagan, Ancsa Hannak, Stefania Ionescu
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:152-158, 2026.

Abstract

Social media platforms provide millions of professional content creators with sustainable incomes. This makes recommender systems central to how visibility and revenue are distributed. As for job markets, it is therefore important to ensure these systems distribute exposure fairly. Prior work finds, however, that popularity-based recommendations are unlikely to yield even individually fair outcomes (i.e., where the resulting popularity of creators matches their quality of content) and calls for targeted solutions. To this end, our work introduces ordered pairwise comparison (OPC) as a cold-start fair exploration intervention within two time-steps that ensures equal exposure across creators both in number and in recommendation order. We prove analytically that OPC yields individually fair outcomes for all creators after two time-steps. While subsequent recommendations inevitably reintroduce some bias, our simulations show that the fairness gains persist long-term with fair outcomes remaining substantially more likely than without the intervention. These gains come without any loss in user satisfaction. Altogether, our results suggest that parity in exposure counts during exploration is not enough to ensure fair outcomes for creators and that accounting for the relative order in which creators are presented is a promising intervention.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-jaoua26a, title = {Recommend Me First: How the Relative Ordering of Recommendations Can Lead to Unfair Outcomes for Content Creators}, author = {Jaoua, Salima and Pagan, Nicol\`o and Hannak, Ancsa and Ionescu, Stefania}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {152--158}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/jaoua26a/jaoua26a.pdf}, url = {https://proceedings.mlr.press/v350/jaoua26a.html}, abstract = {Social media platforms provide millions of professional content creators with sustainable incomes. This makes recommender systems central to how visibility and revenue are distributed. As for job markets, it is therefore important to ensure these systems distribute exposure fairly. Prior work finds, however, that popularity-based recommendations are unlikely to yield even individually fair outcomes (i.e., where the resulting popularity of creators matches their quality of content) and calls for targeted solutions. To this end, our work introduces ordered pairwise comparison (OPC) as a cold-start fair exploration intervention within two time-steps that ensures equal exposure across creators both in number and in recommendation order. We prove analytically that OPC yields individually fair outcomes for all creators after two time-steps. While subsequent recommendations inevitably reintroduce some bias, our simulations show that the fairness gains persist long-term with fair outcomes remaining substantially more likely than without the intervention. These gains come without any loss in user satisfaction. Altogether, our results suggest that parity in exposure counts during exploration is not enough to ensure fair outcomes for creators and that accounting for the relative order in which creators are presented is a promising intervention.} }
Endnote
%0 Conference Paper %T Recommend Me First: How the Relative Ordering of Recommendations Can Lead to Unfair Outcomes for Content Creators %A Salima Jaoua %A Nicolò Pagan %A Ancsa Hannak %A Stefania Ionescu %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-jaoua26a %I PMLR %P 152--158 %U https://proceedings.mlr.press/v350/jaoua26a.html %V 350 %X Social media platforms provide millions of professional content creators with sustainable incomes. This makes recommender systems central to how visibility and revenue are distributed. As for job markets, it is therefore important to ensure these systems distribute exposure fairly. Prior work finds, however, that popularity-based recommendations are unlikely to yield even individually fair outcomes (i.e., where the resulting popularity of creators matches their quality of content) and calls for targeted solutions. To this end, our work introduces ordered pairwise comparison (OPC) as a cold-start fair exploration intervention within two time-steps that ensures equal exposure across creators both in number and in recommendation order. We prove analytically that OPC yields individually fair outcomes for all creators after two time-steps. While subsequent recommendations inevitably reintroduce some bias, our simulations show that the fairness gains persist long-term with fair outcomes remaining substantially more likely than without the intervention. These gains come without any loss in user satisfaction. Altogether, our results suggest that parity in exposure counts during exploration is not enough to ensure fair outcomes for creators and that accounting for the relative order in which creators are presented is a promising intervention.
APA
Jaoua, S., Pagan, N., Hannak, A. & Ionescu, S.. (2026). Recommend Me First: How the Relative Ordering of Recommendations Can Lead to Unfair Outcomes for Content Creators. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:152-158 Available from https://proceedings.mlr.press/v350/jaoua26a.html.

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