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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| Autori principali: | , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI AG
2025-09-01
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| Serie: | Computation |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/2079-3197/13/9/213 |
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