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TopIn the literature, and according to (Gediminas, Adomavicius, & Tuzhilin, 2005), RS can be classified into three classes: content-based (Sánchez, 2013), collaborative filtering (Gediminas, Adomavicius, & Tuzhilin, 2005), (Francesco Ricci & Kantor, 2011) and hybrid approaches (Burke, 2002). In collaborative filtering, Matrix Factorization (MF) (Salakhutdinov & Mnih, 2008) is considered as a good method of predicting the missing ratings. According to this method, the data are organized in a “User x Item” matrix, as illustrated by Figure 1.
Figure 1. A structure of a rating matrix
In Figure 1, the rows represent the users “
, ... 
” and the columns constitute the items “
, ... 
”. This matrix is called rating matrix, denoted by 
, which m and n refers to the number of users and items respectively. Each row of this matrix corresponds to a specified user u, and each column corresponds to a specified item j while the intersection of a row and a column corresponds to a rating value 
of a user’s rating u given to an item j. As we can notice, the matrix R is sparse (more than 99% of the entries are missing), and the aim of a recommender system is to predict the missing entries. Once a recommendation approach is applied and a missing rating of a user u about an item j is calculated, we are talking about a predicted rating 
.