Recommendations with minimum exposure guarantees: A post-processing framework

• Main Authors: LOPES, Ramon, ALVES, Rodrigo, LEDENT, Antoine, SANTOS, Rodrygo L. T., KLOFT, Marius
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Exposure; Fairness; Integer linear programming; Recommender systems; Databases and Information Systems; Numerical Analysis and Scientific Computing; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/8182; https://ink.library.smu.edu.sg/context/sis_research/article/9185/viewcontent/Recom_min_exposure_guarantees_sv.pdf
• Institution: Singapore Management University

Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ra...