A flexible framework for evaluating user and item fairness in recommender systems

One common characteristic of research works focused on fairness evaluation (in machine learning) is that they call for some form of parity (equality) either in treatment—meaning they ignore the information about users’ memberships in protected classes during training—or in impact—by enforcing propor...

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Veröffentlicht in:	User modeling and user-adapted interaction Jg. 31; H. 3; S. 457 - 511
Hauptverfasser:	Deldjoo, Yashar, Anelli, Vito Walter, Zamani, Hamed, Bellogín, Alejandro, Di Noia, Tommaso
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.07.2021 Springer Nature B.V
Schlagworte:	Algorithms Computer Science Design Entropy (Information theory) Equality Machine learning Management of Computing and Information Systems Multimedia Information Systems Recommender systems Statistical analysis User Interfaces and Human Computer Interaction
ISSN:	0924-1868, 1573-1391
Online-Zugang:	Volltext
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