doi:10.48550/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.">

poismf: Factorization of Sparse Counts Matrices Through Poisson Likelihood (original) (raw)

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) <doi:10.48550/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.

Version: 0.4.0-4
Imports: Matrix (≥ 1.3), methods
Published: 2023-03-26
DOI: 10.32614/CRAN.package.poismf
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 <david.cortes.rivera at gmail.com>
BugReports: https://github.com/david-cortes/poismf/issues
License: BSD_2_clause + file
Copyright: see file
URL: https://github.com/david-cortes/poismf
NeedsCompilation: yes
CRAN checks: poismf results

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