{ "_id": "6a495c9577d6db3f2f9648f1", "Package": "cmfrec", "Type": "Package", "Title": "Collective Matrix Factorization for Recommender Systems", "Version": "3.5.1-3", "Authors@R": "c(\nperson(given=\"David\", family=\"Cortes\", role=c(\"aut\", \"cre\", \"cph\"),\nemail=\"david.cortes.rivera@gmail.com\"),\nperson(given=\"Jorge\", family=\"Nocedal\", role=\"cph\",\ncomment=\"Copyright holder of included LBFGS library\"),\nperson(given=\"Naoaki\", family=\"Okazaki\", role=\"cph\",\ncomment=\"Copyright holder of included LBFGS library\"),\nperson(given=\"David\", family=\"Blackman\", role=\"cph\",\ncomment=\"Copyright holder of original Xoshiro code\"),\nperson(given=\"Sebastiano\", family=\"Vigna\", role=\"cph\",\ncomment=\"Copyright holder of original Xoshiro code\"),\nperson(given=\"NumPy\", family=\"Developers\", role=\"cph\",\ncomment=\"Copyright holder of formatted ziggurat tables\")\n)", "Maintainer": "David Cortes ", "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\nmulti-way factorization, Singh, Gordon, (2008)\n) tries to approximate a\n(potentially very sparse or having many missing values) matrix\n'X' as the product of two low-dimensional matrices, optionally\naided with secondary information matrices about rows and/or\ncolumns of 'X', which are also factorized using the same latent\ncomponents. The intended usage is for recommender systems,\ndimensionality reduction, and missing value imputation.\nImplements extensions of the original model (Cortes, (2018)\n) and can produce different factorizations\nsuch as the weighted 'implicit-feedback' model (Hu, Koren,\nVolinsky, (2008) ), the\n'weighted-lambda-regularization' model, (Zhou, Wilkinson,\nSchreiber, Pan, (2008) ), or\nthe enhanced model with 'implicit features' (Rendle, Zhang,\nKoren, (2019) ), with or without side\ninformation. Can use gradient-based procedures or\nalternating-least squares procedures (Koren, Bell, Volinsky,\n(2009) ), with either a Cholesky\nsolver, a faster conjugate gradient solver (Takacs, Pilaszy,\nTikk, (2011) ), or a non-negative\ncoordinate descent solver (Franc, Hlavac, Navara, (2005)\n), providing efficient methods for\nsparse and dense data, and mixtures thereof. 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