Other packages > Find by keyword >

cmfrec  

Collective Matrix Factorization for Recommender Systems
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("cmfrec", "3.5.1-3")



Attach the package and use:
library("cmfrec")
Maintained by
David Cortes
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-11-23
Latest Update: 2023-12-09
Description:
Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) ) tries to approximate a (potentially very sparse or having many missing values) matrix 'X' as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of 'X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) ) and can produce different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky, (2008) ), the 'weighted-lambda-regularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) ), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) ), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) ), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) ), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) ), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data.
How to cite:
David Cortes (2020). cmfrec: Collective Matrix Factorization for Recommender Systems. R package version 3.5.1-3, https://cran.r-project.org/web/packages/cmfrec. Accessed 26 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:27), 2.3.0 (2020-11-23 10:50), 2.3.2 (2020-11-23 17:30), 2.4.1 (2021-01-06 11:50), 2.4.2 (2021-01-10 16:20), 2.4.5 (2021-03-01 19:40), 3.1.0 (2021-05-20 22:30), 3.1.2 (2021-06-28 06:50), 3.2.1 (2021-07-29 20:50), 3.2.2-1 (2021-09-26 06:30), 3.2.2-2 (2021-11-07 21:10), 3.2.2 (2021-07-30 10:00), 3.3.0 (2022-01-03 14:40), 3.3.1 (2022-01-05 16:50), 3.4.1 (2022-02-10 01:30), 3.4.2 (2022-02-10 15:10), 3.4.3-2 (2022-10-25 19:35), 3.4.3 (2022-07-09 17:50), 3.5.0 (2022-11-26 16:10), 3.5.1-1 (2023-04-11 19:00), 3.5.1-2 (2023-11-28 19:10), 3.5.1 (2023-03-08 23:10)
Other packages that cited cmfrec R package
View cmfrec citation profile
Other R packages that cmfrec depends, imports, suggests or enhances
Complete documentation for cmfrec
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

rts2  
Log-Gaussian Cox Process Models with Approximations
Supports modelling real-time case data to facilitate the real-time surveillance of infectious disea ...
Download / Learn more Package Citations See dependency  
nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
SADEG  
Stability Analysis in Differentially Expressed Genes
We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequence ...
Download / Learn more Package Citations See dependency  
LSDsensitivity  
Sensitivity Analysis Tools for 'LSD' Simulations
Tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD ...
Download / Learn more Package Citations See dependency  
MARSS  
Multivariate Autoregressive State-Space Modeling
The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstraine ...
Download / Learn more Package Citations See dependency  

29,218

R Packages

247,686

Dependencies

77,281

Author Associations

29,219

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA