Equal Experience in Recommender Systems

We explore the fairness issue that arises in recommender systems. Biased data due to inherent stereotypes of particular groups (e.g., male students' average rating on mathematics is often higher than that on humanities, and vice versa for females) may yield a limited scope of suggested items to...

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Bibliographische Detailangaben

Hauptverfasser:	Cho, Jaewoong, Choi, Moonseok, Suh, Changho
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Information Retrieval Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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