Quotation Ashokan, Ashwathy, Haas, Christian. 2021. Fairness metrics and bias mitigation strategies for rating predictions. Information Processing & Management. 58 (5)




Algorithm fairness is an established line of research in the machine learning domain with substantial work while the equivalent in the recommender system domain is relatively new. In this article, we consider rating-based recommender systems which model the recommendation process as a prediction problem. We consider different types of biases that can occur in this setting, discuss various fairness definitions, and also propose a novel bias mitigation strategy to address potential unfairness in a rating-based recommender system. Based on an analysis of fairness metrics used in machine learning and a discussion of their applicability in the recommender system domain, we map the proposed metrics from the two domains and identify commonly used concepts and definitions of fairness. Finally, to address unfairness and potential bias against certain groups in a recommender system, we develop a bias mitigation algorithm and conduct case studies on one synthetic and one empirical dataset to show its effectiveness. Our results show that unfairness can be significantly lowered through our approach and that bias mitigation is a fruitful area of research for recommender systems.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Information Processing & Management
Citation Index SSCI
Language English
Title Fairness metrics and bias mitigation strategies for rating predictions
Volume 58
Number 5
Year 2021
URL https://api.elsevier.com/content/article/PII:S0306457321001369?httpAccept=text/xml
DOI http://dx.doi.org/10.1016/j.ipm.2021.102646
Open Access N


Haas, Christian (Details)
Ashokan, Ashwathy (University of Nebraska - Omaha, United States/USA)
Institute for Corporate Governance IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1122 Artificial intelligence (Details)
1127 Information science (Details)
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