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.

TEST 02 - Elsevier's Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.

introduction Share on Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022) Authors: Toine Bogers Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , David Graus Randstad Groep Nederland, Netherlands Randstad Groep Nederland, NetherlandsView Profile , Mesut Kaya Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , Francisco Gutiérrez Computer Science, KU Leuven, Belgium Computer Science, KU Leuven, BelgiumView Profile , Sepideh Mesbah Randstad Groep Nederland, Netherlands Randstad Groep Nederland, NetherlandsView Profile , Chris Johnson Indeed, United States Indeed, United StatesView Profile Authors Info & Claims RecSys '22: Proceedings of the 16th ACM Conference on Recommender SystemsSeptember 2022Pages 671–674https://doi.org/10.1145/3523227.3547414Published:13 September 2022Publication History 0citation112DownloadsMetricsTotal Citations0Total Downloads112Last 12 Months42Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access