As discussed in the previous section, collaborative filtering is a simple yet very effective approach for predicting and recommending items to users. If we look closely, the algorithms work on input data, which is nothing but a matrix representation of the user ratings for different products.
Bringing in a mathematical perspective into the picture, matrix factorization is a technique to manipulate matrices and identify latent or hidden features from the data represented in the matrix. Building on the same concept, let us use matrix factorization as the basis for predicting ratings for items which the user has not yet rated.
Matrix factorization refers to the identification of two or more matrices such that when these matrices are multiplied we get the original matrix. Matrix factorization, as mentioned earlier, can be used to discover latent features between two different kinds of entities. We will understand and use the concepts of matrix...