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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]
[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 06 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
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Other R packages that cmfrec depends,
imports, suggests or enhances
Complete documentation for cmfrec
Functions, R codes and Examples using
the cmfrec R package
Some associated functions: CMF.from.model.matrices . cmfrec . drop.nonessential.matrices . factors . factors_single . fit . imputeX . item_factors . precompute.for.predictions . predict.cmfrec . predict_new . predict_new_items . print.cmfrec . summary.cmfrec . swap.users.and.items . topN .
Some associated R codes: cmfrec.R . factors.R . factors_single.R . fit.R . helpers.R . impute.R . items.R . methods.R . other.R . predict.R . predict_new.R . topN.R . Full cmfrec package functions and examples
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