Patent ID: 8560486

Claim:
A content recommendation method based on psychological factors from a consumption profile of a user which is applied in a website for downloading contents through a mobile telephone communications network, the method comprising: (i) detecting the consumption profile of the user that accesses the website for downloading contents; (ii) using a psychographics driver to calculate a psychological profile of the user according to five super traits of a Big Five model, said psychological profile being stored for use in a current query and subsequent queries; and (iii) cross-matching the psychological profile resulting from (ii) and a content data matrix, predefined in a content database, and obtaining as a result of the cross-matching a preferred access of the user to those contents which best adapt to the psychological profile of the user, wherein in (ii), to calculate the five super traits of the Big Five model, a set of business variables associated with each of the five super rafts is determined according to results obtained in (i), a theoretical weight and a reliability weight are assigned to each variable in the set of business variables associated with each of the five super traits based on a number of individuals who have a value in the variable divided by a total number of individuals, and wherein once the theoretical weight and the reliability weight are assigned to each variable in the set of the business variables associated with each of the five super traits, a linear equation is used to calculate a normalized score between −1 and 1 for each individual in each of the five super traits of the Big Five model, wherein said linear equation is unique for each of the five super traits: C i = ∑ j = 1 k ⁢ [ WT j · ( x ji - X _ j s j ⁡ ( z max - z min ) ) · WR j ] ∑ j = 1 k ⁢ ( D ji ·  WT j  · WR j ·  E j  where: C i is a score in each super trait of a user i; k is a total number of variables from which the super trait will be calculated; WT j is a weight for a variable j; x ji is a value of the user i in the variable j; X j is a mean of the variable j: s j is a standard deviation of the variable j; z max is a standard score of the maximum value of the variable j; z min is a standard score of the minimum value of the variable j; D ji is a binary variable that is 1 if the user i has data in the variable j and 0 if the user i does not have data in the variable j; WR j is a reliability weight of the variable j; and E j is an absolute value of a defined scale factor.