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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Egile Nagusiak:	Rodolfo Bojorque, Remigio Hurtado, Miguel Arcos-Argudo, Mauricio Ortiz
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2026-04-01
Saila:	Information
Gaiak:	recommender systems anomaly detection unsupervised learning ensemble methods user behavior analysis
Sarrera elektronikoa:	https://www.mdpi.com/2078-2489/17/5/426
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