poismf

Description

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.

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Maintainer: ‘David Cortes <david.cortes.rivera@gmail.com>’

The Description field contains
  bag-of-words-based topic modeling) (Cortes, (2018) <arXiv:1811.01908>),
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘David Cortes <david.cortes.rivera@gmail.com>’

The Description field contains
  bag-of-words-based topic modeling) (Cortes, (2018) <arXiv:1811.01908>),
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Maintainer: ‘David Cortes <david.cortes.rivera@gmail.com>’

The Description field contains
  bag-of-words-based topic modeling) (Cortes, (2018) <arXiv:1811.01908>),
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘David Cortes <david.cortes.rivera@gmail.com>’

The Description field contains
  bag-of-words-based topic modeling) (Cortes, (2018) <arXiv:1811.01908>),
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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