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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]
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 16 Jul. 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)
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