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Descripción
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| Recommender Systems present a high-level of sparsity in their ratings matrices. The collaborative filtering sparse data makes it difficult to: 1) compare elements using memory-based solutions; 2) obtain precise models using model-based solutions; 3) get accurate predictions; and 4) properly cluster elements. We propose the use of a Bayesian non-negative matrix factorization (BNMF) method to improve the current clustering results in the collaborative filtering area. We also provide an original pre-clustering algorithm adapted to the proposed probabilistic method. Results obtained using several open data sets show: 1) a conclusive clustering quality improvement when BNMF is used, compared with the classical matrix factorization or to the improved KMeans results; 2) a higher predictions accuracy using matrix factorization based methods than using improved KMeans; and 3) better BNMF execution times compared with those of the classic matrix factorization, and an additional improvement when using the proposed pre-clustering algorithm. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Ieee Access |
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ISSN
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2169-3536 |
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Factor de impacto JCR
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3,244 |
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Información de impacto
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Datos JCR del año 2016 |
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Volumen
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6 |
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DOI
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10.1109/ACCESS.2017.2788138 |
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Número de revista
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Desde la página
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3549 |
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Hasta la página
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3564 |
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Mes
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DICIEMBRE |
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Ranking
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