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.
Package details |
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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@gmail.com> |
License | BSD_2_clause + file LICENSE |
Version | 0.4.0-4 |
URL | https://github.com/david-cortes/poismf |
Package repository | View on CRAN |
Installation |
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