  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.
