Self-adaptive, self-trained computer engines based on machine learning and methods of use thereof

A computer-implemented method, comprising:

• obtaining, by at least one processor, at least one portion of initial content data from at least one content database;
• wherein the at least one content database comprises the initial content data derived from a plurality of electronic content feeds from a plurality of distinct electronic sources;
• applying, by the at least one processor, at least one machine learning model to transform the at least one portion of the initial content data into a N-dimensional vector space;
• applying, by the at least one processor, at least one clustering technique to the N-dimensional vector space to generate a cluster model that defines a plurality of vector clusters;
• obtaining, by the at least one processor, at least one portion of first subsequent content data from the at least one content database;
• wherein the first subsequent content data is content data received from the plurality of electronic content feeds from the plurality of distinct electronic sources after the cluster model has been generated during at least one first time period;
• associating, by the at least one processor, each data element of at least one portion of the first subsequent content data with each respective vector cluster of the plurality of vector clusters of the cluster model to generate a content data cluster mapping model;
• continuously tracking, for each user, in real-time, by the at least one processor, activity of each respective user regarding at least one data element from at least one respective portion of the first subsequent content data during each respective time period to form a plurality of respective continuously self-adapting user-specific, time-specific dynamic cluster mapping model;
• and
• automatically providing, by the at least one processor, to each respective user, based, at least in part, on each respective continuously self-adapting user-specific, time-specific dynamic cluster mapping model, at least one relevant subscription recommendation regarding at least one recommended content feed without a need that each respective user is to take an action that would identify a desire by each respect user to be subscribed to the at least one recommended content feed.