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: | 0.4.0-1 |
| Imports: | Matrix (≥ 1.3), methods |
| Published: | 2022-08-15 |
| 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 |
| Reference manual: | poismf.pdf |
| Package source: | poismf_0.4.0-1.tar.gz |
| Windows binaries: | r-devel: poismf_0.4.0-1.zip, r-release: poismf_0.4.0-1.zip, r-oldrel: poismf_0.4.0-1.zip |
| macOS binaries: | r-release (arm64): poismf_0.4.0-1.tgz, r-oldrel (arm64): poismf_0.4.0-1.tgz, r-release (x86_64): poismf_0.4.0-1.tgz, r-oldrel (x86_64): poismf_0.4.0-1.tgz |
| Old sources: | poismf archive |
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