Fairness metrics and bias mitigation strategies for rating predictions

Ashwathy Ashokan, Christian Haas

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number102646
JournalInformation Processing and Management
Volume58
Issue number5
DOIs
StatePublished - Sep 2021

Keywords

  • Algorithmic fairness
  • Bias mitigation
  • Fairness metrics
  • Recommender systems

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences

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