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Facets of Fairness in Search and Recommendation

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1245)

Abstract

Several recent works have highlighted how search and recommender systems exhibit bias along different dimensions. Counteracting this bias and bringing a certain amount of fairness in search is crucial to not only creating a more balanced environment that considers relevance and diversity but also providing a more sustainable way forward for both content consumers and content producers. This short paper examines some of the recent works to define relevance, diversity, and related concepts. Then, it focuses on explaining the emerging concept of fairness in various recommendation settings. In doing so, this paper presents comparisons and highlights contracts among various measures, and gaps in our conceptual and evaluative frameworks.

Keywords

Search bias Fairness Evaluation metrics Fairness in recommendation Fair ranking 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of WashingtonSeattleUSA
  2. 2.Rutgers UniversityNew BrunswickUSA

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