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sgdGMF  

Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent
View on CRAN: Click here


Download and install sgdGMF package within the R console
Install from CRAN:
install.packages("sgdGMF")

Install from Github:
library("remotes")
install_github("cran/sgdGMF")

Install by package version:
library("remotes")
install_version("sgdGMF", "1.0")



Attach the package and use:
library("sgdGMF")
Maintained by
Cristian Castiglione
[Scholar Profile | Author Map]
First Published: 2025-02-13
Latest Update: 2025-02-13
Description:
Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, <http://jmlr.org/papers/v23/20-1104.html>).
How to cite:
Cristian Castiglione (2025). sgdGMF: Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent. R package version 1.0, https://cran.r-project.org/web/packages/sgdGMF. Accessed 11 May. 2025.
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