Facets of Fairness in Search and Recommendation
arXiv:2008.01194
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.
References in corpus (7)
- Fairness of Exposure in Rankings
- FA*IR: A Fair Top-k Ranking Algorithm
- Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
- Multisided Fairness for Recommendation
- Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination
- Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness
- Artist and style exposure bias in collaborative filtering based music recommendations