TY - JOUR
T1 - Recommender systems clustering using Bayesian non negative matrix factorization
AU - Bobadilla, Jesús
AU - Bojorque, Rodolfo
AU - Esteban, Antonio Hernando
AU - Hurtado, Remigio
PY - 2017/12/28
Y1 - 2017/12/28
N2 - © 2013 IEEE. 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.
AB - © 2013 IEEE. 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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U2 - 10.1109/ACCESS.2017.2788138
DO - 10.1109/ACCESS.2017.2788138
M3 - Article
SP - 3549
EP - 3564
JO - IEEE Access
JF - IEEE Access
ER -