.png){kind=icon}
.png){kind=icon}
Return
Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2017.2788138.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Bayesian NMF
collaborative filtering
hard clustering
matrix factorization
pre-clustering
recommender systems
sparse data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W