A Comparative Study of Unsupervised Machine Learning and Deep Learning Techniques for Anomaly Detection in Recommender Systems

Recommender systems are increasingly exposed to anomalous user behavior that can distort recommendation outcomes and compromise system reliability. In real-world settings, explicit labels identifying malicious activity are rarely available, motivating the adoption of unsupervised detection approache...

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Autori principali:	Rodolfo Bojorque, Remigio Hurtado, Miguel Arcos-Argudo, Mauricio Ortiz
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2026-04-01
Serie:	Information
Soggetti:	recommender systems anomaly detection unsupervised learning ensemble methods user behavior analysis
Accesso online:	https://www.mdpi.com/2078-2489/17/5/426
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