In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness. We introduce inferiority, a novel (un)fairness measure quantifying a user’s competitive disadvantage for their recommended items. Inferiority complements envy, a fairness notion measuring preference for others’ recommendations. We combine inferiority and envy with utility, an accuracy-related measure of aggregated relevancy scores. Since these measures are non-differentiable, we reformulate them using a probabilistic interpretation of recommender systems, yielding differentiable versions. We combine these loss functions in a multi-objective optimization problem called FEIR (Fairness through Envy and Inferiority Reduction), applied as post-processing for standard recommender systems. Experiments on synthetic and real-world data demonstrate that our approach improves trade-offs between inferiority, envy, and utility compared to naive recommendations and the baseline methods.
CITATION STYLE
Li, N., Kang, B., Lijffijt, J., & De Bie, T. (2023). FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources. In CEUR Workshop Proceedings (Vol. 3490). CEUR-WS. https://doi.org/10.1145/3643891
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