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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 sparseor 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) <doi:10.48550/arXiv.1809.00366>) and can producedifferent 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) <doi:10.48550/arXiv.1905.01395>), with or without side information. Can use gradient-basedprocedures 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-negativecoordinate 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 functionalityfor cold-start recommendations and imputation of 2D data.

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