No Bias Left Behind: Fairness Testing for Deep Recommender Systems Targeting General Disadvantaged Groups

Recommender systems play an increasingly important role in modern society, powering digital platforms that suggest a wide array of content, from news and music to job listings, and influencing many aspects of daily life. To improve personalization, these systems often use demographic information. Ho...

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Vydané v:	Proceedings of the ACM on software engineering Ročník 2; číslo ISSTA; s. 1607 - 1629
Hlavní autori:	Wu, Zhuo, Wang, Zan, Luo, Chuan, Du, Xiaoning, Chen, Junjie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY, USA ACM 22.06.2025
Predmet:	Information systems Recommender systems Search-based software engineering Software and its engineering Software testing and debugging Fairness Testing Deep Recommender System Search-based Software Testing
ISSN:	2994-970X, 2994-970X
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