Fairness in recommender systems: research landscape and future directions

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-base...

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Veröffentlicht in:User modeling and user-adapted interaction Jg. 34; H. 1; S. 59 - 108
Hauptverfasser: Deldjoo, Yashar, Jannach, Dietmar, Bellogin, Alejandro, Difonzo, Alessandro, Zanzonelli, Dario
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.03.2024
Springer Nature B.V
Schlagworte:
Computer Science
Interdisciplinary studies
Management of Computing and Information Systems
Multimedia Information Systems
Questions
Recommender systems
User Interfaces and Human Computer Interaction
Survey
Recommender systems
Fairness
ISSN:0924-1868, 1573-1391
Online-Zugang:Volltext
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Zusammenfassung: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, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.
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ISSN:0924-1868
1573-1391
DOI:10.1007/s11257-023-09364-z