Package: cmfrec
Type: Package
Title: Collective Matrix Factorization for Recommender Systems
Version: 3.5.1-3
Authors@R: c(
  person(given="David", family="Cortes", role=c("aut", "cre", "cph"),
         email="david.cortes.rivera@gmail.com"),
  person(given="Jorge", family="Nocedal", role="cph",
         comment="Copyright holder of included LBFGS library"),
  person(given="Naoaki", family="Okazaki", role="cph",
         comment="Copyright holder of included LBFGS library"),
  person(given="David", family="Blackman", role="cph",
         comment="Copyright holder of original Xoshiro code"),
  person(given="Sebastiano", family="Vigna", role="cph",
         comment="Copyright holder of original Xoshiro code"),
  person(given="NumPy", family="Developers", role="cph",
         comment="Copyright holder of formatted ziggurat tables")
  )
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/cmfrec
BugReports: https://github.com/david-cortes/cmfrec/issues
Description: Collective matrix factorization (a.k.a. multi-view or multi-way factorization,
	Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) 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) <arXiv:1809.00366>) and can produce
	different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky,
	(2008) <doi:10.1109/ICDM.2008.22>), the 'weighted-lambda-regularization' model,
	(Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>),
	or the enhanced model with 'implicit features' (Rendle, Zhang,
	Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradient-based
	procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009)
	<doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver
	(Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative
	coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>),
	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.
License: MIT + file LICENSE
Suggests: Matrix, MatrixExtra, RhpcBLASctl, recosystem (>= 0.5),
        recommenderlab (>= 0.2-7), MASS, knitr, rmarkdown, kableExtra
VignetteBuilder: knitr
RoxygenNote: 7.2.3
NeedsCompilation: yes
Packaged: 2023-12-09 18:00:10 UTC; david
Author: David Cortes [aut, cre, cph],
  Jorge Nocedal [cph] (Copyright holder of included LBFGS library),
  Naoaki Okazaki [cph] (Copyright holder of included LBFGS library),
  David Blackman [cph] (Copyright holder of original Xoshiro code),
  Sebastiano Vigna [cph] (Copyright holder of original Xoshiro code),
  NumPy Developers [cph] (Copyright holder of formatted ziggurat tables)
Repository: CRAN
Date/Publication: 2023-12-09 18:30:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-25 17:42:44 UTC; unix
Archs: cmfrec.so.dSYM
