Collective Matrix Factorization for Recommender Systems

Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) ) tries to approximate a matrix 'X' as the product of two low-dimensional matrices 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.


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2.4.5 by David Cortes, 2 months ago

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Authors: David Cortes [aut, cre, cph] , Jorge Nocedal [cph] , Naoaki Okazaki [cph]

Documentation:   PDF Manual  

MIT + file LICENSE license

Suggests Matrix, rsparse, recommenderlab, MASS

Enhances SparseM

See at CRAN