Matrix Factorization-Based Clustering for Sparse Data in Recommender Systems: A Comparative Study

Clustering techniques significantly enhance recommender systems by improving predictive accuracy and interpretability, particularly in sparse, high-dimensional datasets. This research presents a comprehensive comparative analysis of traditional clustering methods such as K-means and Fuzzy C-Means (F...

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Egile Nagusiak:	Rodolfo Bojorque, Remigio Hurtado
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2025-09-01
Saila:	Computation
Gaiak:	recommender systems matrix factorization clustering
Sarrera elektronikoa:	https://www.mdpi.com/2079-3197/13/9/213
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