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) <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: 0.4.0-2
Imports: Matrix (≥ 1.3), methods
Published: 2022-10-25
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_0.4.0-2.tar.gz
Windows binaries: r-devel: poismf_0.4.0-2.zip, r-release: poismf_0.4.0-2.zip, r-oldrel: poismf_0.4.0-2.zip
macOS binaries: r-release (arm64): poismf_0.4.0-2.tgz, r-oldrel (arm64): poismf_0.4.0-2.tgz, r-release (x86_64): poismf_0.4.0-2.tgz, r-oldrel (x86_64): poismf_0.4.0-2.tgz
Old sources: poismf archive

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