Positive-Unlabeled Learning in Implicit Feedback from Data Missing-Not-At-Random Perspective

The lack of explicit negative labels issue is a prevalent challenge in numerous domains, including CV, NLP, and Recommender Systems (RSs). To address this challenge, many negative sample completion methods are proposed, such as optimizing sample distribution through pseudo-negative sampling and conf...

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Detalles Bibliográficos

Principais autores:	Sichao Wang, Tianyu Xia, Lingxiao Yang
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2025-12-01
Series:	Entropy
Assuntos:	recommender systems implicit feedback missing-not-at-random positive-unlabeled learning
Acceso en liña:	https://www.mdpi.com/1099-4300/28/1/41
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