Package: poismf 0.4.0-4

David Cortes

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.

Authors:David Cortes [aut, cre, cph], Jean-Sebastien Roy [cph], Stephen Nash [cph]

poismf_0.4.0-4.tar.gz
poismf_0.4.0-4.tar.gz(r-4.5-noble)poismf_0.4.0-4.tar.gz(r-4.4-noble)
poismf_0.4.0-4.tgz(r-4.4-emscripten)poismf_0.4.0-4.tgz(r-4.3-emscripten)
poismf.pdf |poismf.html✨
poismf/json (API)

# Install 'poismf' in R:
install.packages('poismf', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/david-cortes/poismf/issues

Uses libs:
  • openblas– Optimized BLAS
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblasopenmp

1.00 score 384 downloads 11 exports 2 dependencies

Last updated 2 years agofrom:451fcff9b1. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 22 2025
R-4.5-linux-x86_64OKMar 22 2025
R-4.4-linux-x86_64OKMar 22 2025

Exports:factorsfactors.singleget.factor.matricesget.model.mappingspoismfpoismf_unsafepredict.poismfprint.poismfsummary.poismftopNtopN.new

Dependencies:latticeMatrix

Citation

To cite package ‘poismf’ in publications use:

Cortes D (2023). poismf: Factorization of Sparse Counts Matrices Through Poisson Likelihood. R package version 0.4.0-4, https://CRAN.R-project.org/package=poismf.

Corresponding BibTeX entry: 

  @Manual{,
    title = {poismf: Factorization of Sparse Counts Matrices Through
      Poisson Likelihood},
    author = {David Cortes},
    year = {2023},
    note = {R package version 0.4.0-4},
    url = {https://CRAN.R-project.org/package=poismf},
  }