Package: cmfrec 3.5.1-3

David Cortes

cmfrec: Collective Matrix Factorization for Recommender Systems

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.

Authors:David Cortes [aut, cre, cph], Jorge Nocedal [cph], Naoaki Okazaki [cph], David Blackman [cph], Sebastiano Vigna [cph], NumPy Developers [cph]

cmfrec_3.5.1-3.tar.gz
cmfrec_3.5.1-3.tar.gz(r-4.5-noble)cmfrec_3.5.1-3.tar.gz(r-4.4-noble)
cmfrec_3.5.1-3.tgz(r-4.4-emscripten)cmfrec_3.5.1-3.tgz(r-4.3-emscripten)
cmfrec.pdf |cmfrec.html
cmfrec/json (API)

# Install 'cmfrec' in R:
install.packages('cmfrec', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/david-cortes/cmfrec/issues

Uses libs:
  • openblas– Optimized BLAS
  • openmp– GCC OpenMP (GOMP) support library

2.60 score 20 scripts 490 downloads 18 exports 0 dependencies

Last updated 1 years agofrom:807a733f7a. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-linux-x86_64OKOct 31 2024

Exports:CMFCMF_implicitCMF.from.model.matricesContentBaseddrop.nonessential.matricesfactorsfactors_singleimputeXitem_factorsMostPopularOMF_explicitOMF_implicitprecompute.for.predictionspredict_newpredict_new_itemsswap.users.and.itemstopNtopN_new

Dependencies:

Matrix Factorization with Side Info

Rendered fromcmfrec_vignette.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2022-11-26
Started: 2021-05-20

Readme and manuals

Help Manual

Help pageTopics
Create a CMF model object from fitted matricesCMF.from.model.matrices
cmfrec packagecmfrec-package cmfrec
Drop matrices that are not used for predictiondrop.nonessential.matrices
Calculate latent factors on new datafactors factors.CMF factors.CMF_implicit factors.ContentBased factors.OMF_explicit factors.OMF_implicit
Calculate latent factors for a new userfactors_single factors_single.CMF factors_single.CMF_implicit factors_single.ContentBased factors_single.OMF_explicit factors_single.OMF_implicit
Matrix Factorization ModelsCMF CMF_implicit ContentBased fit_models MostPopular OMF_explicit OMF_implicit
Impute missing entries in `X` dataimputeX
Determine latent factors for a new itemitem_factors
Precompute matrices to use for predictionsprecompute.for.predictions
Predict entries in new `X` datapredict_new predict_new.CMF predict_new.CMF_implicit predict_new.ContentBased predict_new.OMF_explicit predict_new.OMF_implicit
Predict new columns of `X` given item attributespredict_new_items
Predict entries in the factorized `X` matrixpredict.cmfrec
Get information about factorization modelprint.cmfrec
Get information about factorization modelsummary.cmfrec
Swap users and items in the modelswap.users.and.items
Calulate top-N predictions for a new or existing usertopN topN_new topN_new.CMF topN_new.CMF_implicit topN_new.ContentBased topN_new.OMF_explicit topN_new.OMF_implicit