易于
发表于 2025-3-28 17:02:33
,Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Apprn daran, dass sie nicht zufrieden stellend erk- ren können, warum Lernen dermassen erfolgreich ist, wenn es um die Wahl optimaler Handlungs- und Verhaltenseisen in konkreten Situationen geht. Nicht das (kognitive) F978-3-531-16374-1978-3-531-91312-4
冲击力
发表于 2025-3-28 21:02:04
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Annotate
发表于 2025-3-29 02:17:08
,The Impact of Recommender System and Users’ Behaviour on Choices’ Distribution and Quality,978-3-531-94185-1
六边形
发表于 2025-3-29 04:41:46
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柱廊
发表于 2025-3-29 11:02:01
FARGO: A Fair, Context-AwaRe, Group RecOmmender System,978-3-322-87566-2
countenance
发表于 2025-3-29 13:34:33
Advances in Bias and Fairness in Information RetrievalThird International
constellation
发表于 2025-3-29 18:34:36
Conclusion: A Short-Form Future?large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than
无瑕疵
发表于 2025-3-29 22:10:06
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古代
发表于 2025-3-30 00:31:44
Conclusion: A Short-Form Future?sults for the same queries depending on the country in which they reside. These results indicate that using crowdsourcing platforms to study system behavior, in a way that preserves participant privacy, is a viable approach to obtain insights into black-box systems, supporting research investigation
有特色
发表于 2025-3-30 07:44:27
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