Facets of Fairness in Search and Recommendation
It addresses fairness issues in search and recommendation systems, which is crucial for creating balanced environments for content consumers and producers, but is incremental as it reviews existing works.
This paper reviews recent works on fairness in search and recommender systems, focusing on defining concepts like relevance and diversity, and highlights gaps in current frameworks for measuring fairness.
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.