Facets of Fairness in Search and Recommendation

TL;DR

This paper reviews recent research on fairness in search and recommendation systems, discussing definitions, measures, and gaps to promote balanced and unbiased content delivery.

Contribution

It provides a comparative analysis of fairness concepts and highlights gaps in current frameworks for evaluating fairness in recommendation systems.

Findings

Identifies different fairness measures and their applications.

Highlights gaps and inconsistencies in current fairness evaluation frameworks.

Emphasizes the importance of balancing relevance, diversity, and fairness.

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.