2022/04/02 by Virginie Do, Nicolas Usunier, Do, Virginie +1 · 2 citations
Business, Management and Accounting · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #Consumer Market Behavior and Pricing #Economic and Environmental Valuation #FOS: Computer and information sciences #Game Theory and Voting Systems #Information Retrieval (cs.IR)
paper · pdf · doi:10.48550/arxiv.2204.06521
openalex publication_date 2022/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
There is growing interest in designing recommender systems that aim at being fair towards item producers or their least satisfied users. Inspired by the domain of inequality measurement in economics, this paper explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for. GGFs weight individuals depending on their ranks in the population, giving more weight to worse-off individuals to promote equality. Depending on these weights, GGFs minimize the Gini index of item exposure to promote equality between items, or focus on the performance on specific quantiles of least satisfied users. GGFs for ranking are challenging to optimize because they are non-differentiable. We resolve this challenge by leveraging tools from non-smooth optimization and projection operators used in differentiable sorting. We present experiments using real datasets with up to 15k users and items, which show that our approach obtains better trade-offs than the baselines on a variety of recommendation tasks and fairness criteria.