A method for dynamic generation of web interfaces contextualized per user, comprising the following steps: i. A Web server, hosting at least one web content, requests credentials from a user; ii. Sending user authentication data from a client web browser to the web server; iii. Sending input parameters from the client web browser to the web server; input parameters relating to: identification of a web content hosted on the web server, user identification and set of interactions between the user and the client web browser, within the scope of the identified web content; iv. Processing of input parameters by the web server to convert said parameters into numerical variables; v. Storage of input parameters in a database; vi. Sending the input parameters formatted into numerical variables to an Artificial Intelligence module by the web server, to generate at least one alternative layout, l k, for the web content, where k represents the number of interactions the user has with the web content; vii. Obtaining a dominant layout, M 0,, by the Artificial Intelligence module; where M o = l 1, l 2, l 3, l 4,..., l k; viii. Sending information regarding the dominant layout from the Artificial Intelligence module to an interface generation module; ix. Generation of a JSON file by the interface generation module; x. Sending the file to the web server which, based on that information, builds an adaptable and customized user interface; xi. Sending the customized interface to the client web browser; and wherein, in step vi., the generation of at least one alternative layout includes: - generating a matrix of interactions, M x k, based on the input parameters; the matrix of interactions, M x k, is M k x y a 1,1 ⋯ a 1, j ⋮ ⋱ ⋮ ai, 1 ⋯ ai, j. Where, a i, j = { 1, if component i and characteristic j exist 0, otherwise, x: user, y: web content, k: No. of interactions, i: No. of components of web content y, j: total No. of characteristics - processing the matrix of interactions, M x k, according to a model, ML, implementing a deep neural network classification algorithm, such that, f ( M k) = l k, f: ML model, M k: Matrices, l k: Layouts, for each user x and for each user interaction k; and wherein, in step vii., the selection of a dominant layout involves applying a trend function to the generated alternative layouts. i. A Web server, hosting at least one web content, requests credentials from a user; ii. Sending user authentication data from a client web browser to the web server; iii. Sending input parameters from the client web browser to the web server; input parameters relating to: identification of a web content hosted on the web server, user identification and set of interactions between the user and the client web browser, within the scope of the identified web content; iv. Processing of input parameters by the web server to convert said parameters into numerical variables; v. Storage of input parameters in a database; vi. Sending the input parameters formatted into numerical variables to an Artificial Intelligence module by the web server, to generate at least one alternative layout, l k, for the web content, where k represents the number of interactions the user has with the web content; vii. Obtaining a dominant layout, M 0,, by the Artificial Intelligence module; where M o = l 1, l 2, l 3, l 4,..., l k; viii. Sending information regarding the dominant layout from the Artificial Intelligence module to an interface generation module; ix. Generation of a JSON file by the interface generation module; x. Sending the file to the web server which, based on that information, builds an adaptable and customized user interface; xi. Sending the customized interface to the client web browser; - generating a matrix of interactions, M x k, based on the input parameters; the matrix of interactions, M x k, is M k x y a 1,1 ⋯ a 1, j ⋮ ⋱ ⋮ ai, 1 ⋯ ai, j. Where, a i, j = { 1, if component i and characteristic j exist 0, otherwise, x: user, y: web content, k: No. of interactions, i: No. of components of web content y, j: total No. of characteristics - processing the matrix of interactions, M x k, according to a model, ML, implementing a deep neural network classification algorithm, such that, f ( M k) = l k, f: ML model, M k: Matrices, l k: Layouts, for each user x and for each user interaction k;