Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling) (Cortes, (2018) <arXiv:1811.01908>), which usually leads to very sparse user and item factors (over 90% zero-valued). Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference.
|Author||David Cortes [aut, cre, cph], Jean-Sebastien Roy [cph] (Copyright holder of included tnc library), Stephen Nash [cph] (Copyright holder of included tnc library)|
|Maintainer||David Cortes <firstname.lastname@example.org>|
|License||BSD_2_clause + file LICENSE|
|Package repository||View on CRAN|
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