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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "poismf" in publications use:'
type: software
license: BSD-2-Clause
title: 'poismf: Factorization of Sparse Counts Matrices Through Poisson Likelihood'
version: 0.4.0-4
doi: 10.32614/CRAN.package.poismf
abstract: 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:
- family-names: Cortes
  given-names: David
  email: david.cortes.rivera@gmail.com
repository: https://CRAN.R-project.org/package=poismf
repository-code: https://github.com/david-cortes/poismf
url: https://github.com/david-cortes/poismf
date-released: '2023-03-26'
contact:
- family-names: Cortes
  given-names: David
  email: david.cortes.rivera@gmail.com