get.model.mappings

Will extract the mapping between IDs passed as `X` to
function `poismf` and row/column positions in the latent factor matrices
and prediction functions.
Such a mapping will only be generated if the `X` passed to `poismf` was a
`data.frame`, otherwise they will not be re-mapped.

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.

David Cortes

poismf

Factorization of Sparse Counts Matrices Through Poisson
Likelihood

Jean-Sebastien Roy

Stephen Nash

get.model.mappings function

<dl><dt>model</dt>
<dd>A Poisson factorization model as returned by `poismf`.</dd></dl>

Arguments

Extract user/row and item/column mappings from Poisson model. — get.model.mappings

<dl>

<dt>model</dt>
<dd>A Poisson factorization model as returned by `poismf`.</dd>

</dl>

get.model.mappings: Extract user/row and item/column mappings from Poisson model.

Description

Usage

Value

Arguments

See Also