poismf: Factorization of Sparse Counts Matrices Through Poisson Likelihood

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
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 LICENSE
Copyright:	see file COPYRIGHTS
URL:	https://github.com/david-cortes/poismf
NeedsCompilation:	yes
CRAN checks:	poismf results

Documentation:

Reference manual:

poismf.pdf

Downloads:

Package source:	poismf_0.4.0-4.tar.gz
Windows binaries:	r-devel: poismf_0.4.0-4.zip, r-release: poismf_0.4.0-4.zip, r-oldrel: poismf_0.4.0-4.zip
macOS binaries:	r-release (arm64): poismf_0.4.0-4.tgz, r-oldrel (arm64): poismf_0.4.0-4.tgz, r-release (x86_64): poismf_0.4.0-4.tgz, r-oldrel (x86_64): poismf_0.4.0-4.tgz
Old sources:	poismf archive

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