Package: cmfrec 3.5.1-3
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:
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')) |
Bug tracker:https://github.com/david-cortes/cmfrec/issues
Last updated 1 years agofrom:807a733f7a. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-linux-x86_64 | OK | Oct 31 2024 |
Exports:CMFCMF_implicitCMF.from.model.matricesContentBaseddrop.nonessential.matricesfactorsfactors_singleimputeXitem_factorsMostPopularOMF_explicitOMF_implicitprecompute.for.predictionspredict_newpredict_new_itemsswap.users.and.itemstopNtopN_new
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Create a CMF model object from fitted matrices | CMF.from.model.matrices |
cmfrec package | cmfrec-package cmfrec |
Drop matrices that are not used for prediction | drop.nonessential.matrices |
Calculate latent factors on new data | factors factors.CMF factors.CMF_implicit factors.ContentBased factors.OMF_explicit factors.OMF_implicit |
Calculate latent factors for a new user | factors_single factors_single.CMF factors_single.CMF_implicit factors_single.ContentBased factors_single.OMF_explicit factors_single.OMF_implicit |
Matrix Factorization Models | CMF CMF_implicit ContentBased fit_models MostPopular OMF_explicit OMF_implicit |
Impute missing entries in `X` data | imputeX |
Determine latent factors for a new item | item_factors |
Precompute matrices to use for predictions | precompute.for.predictions |
Predict entries in new `X` data | predict_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 attributes | predict_new_items |
Predict entries in the factorized `X` matrix | predict.cmfrec |
Get information about factorization model | print.cmfrec |
Get information about factorization model | summary.cmfrec |
Swap users and items in the model | swap.users.and.items |
Calulate top-N predictions for a new or existing user | topN topN_new topN_new.CMF topN_new.CMF_implicit topN_new.ContentBased topN_new.OMF_explicit topN_new.OMF_implicit |