Fairness in recommender systems: research landscape and future directions
Polytechnic University of Bari · University of Klagenfurt · +1 more institution
Abstract
Abstract Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 160 scholarly publications,…
Citation impact
- FWCI
- 41.61
- Percentile
- 100%
- References
- 151
Authors
5Topics & keywords
- Operationalization
- Recommender system
- Normative
- Computer science
- Field (mathematics)
- Context (archaeology)
- Data science
- Value (mathematics)