1. A method of media content recommendation, comprising: generating a first digital mathematical representation of media contents to associate the media contents with a first plurality of words describing the media contents, comprising defining content text documents describing the media contents by said first plurality of words and processing the first plurality of words to generate a first document-word matrix representing said media contents; generating a second digital mathematical representation of context text documents, different from said content text documents, to associate said context text documents with a second plurality of words, comprising processing the second plurality of words to generate a second document-word matrix representing said context text documents, wherein the context text documents include subjects not connected to the media contents; merging the first and second pluralities of words to create a common plurality of words; processing the first and second digital mathematical representations to generate a common digital mathematical representation of the content and context text documents, based on the common plurality of words, comprising processing the first and second document-word matrices and the common plurality of words to generate a third document-word matrix representing the content and context text documents; and providing media content recommendation by processing the common digital mathematical representation, wherein processing the first and second document-word matrices and the common plurality of words to generate a third document-word matrix comprises: decomposing the first document-word matrix into a product of three first matrices using QR factorisation technique while incorporating modifications of term frequency factors and inverse document frequency factors due to updating of said content and context text documents; decomposing the second document-word matrix into a product of three second matrices using QR factorisation technique while incorporating modifications of term frequency factors and inverse document frequency factors due to updating of said content and context text documents; merging the three first matrices and three second matrices to create a common matrix by concatenating the three first matrices as a first element of the common matrix and the three second matrices as a second element of the common matrix; decomposing the common matrix into a product of three common matrices using QR factorisation technique; and defining the third document-word matrix from said product of the three common matrices.