Machine learning to determine the relevance of creative content to a provided set of users and an interactive user interface for improving the relevance

A processing-system-implemented method that uses artificial intelligence to determine a measure of relevance of a client-provided message to client-provided users, the method comprising:

• building a machine-learning model to predict two or more dimensions of a multidimensional profile of a message of creative content, the building using postings on at least one respective media platform of each of a set of users and two or more dimensions of multidimensional profiles of the users of the set;
• receiving a message of creative content from a client;
• using the built machine-learning model to determine the two or more dimensions of a multidimensional signature of the received message;
• receiving from the client client-selected users;
• selecting from the client-selected users a set of client-selected users on whom behavioral footprint data is available;
• retrieving or determining the two or more dimensions of the multidimensional profiles of the set of client-selected users;
• determining the two or more dimensions of a multidimensional signature of the set of client-selected users;
• and
• comparing the two or more dimensions of the signatures of the set of client-selected users and of the received message to determine a measure of relevance of the message of creative content to the set of client-selected users.

A non-transitory machine-readable medium having stored therein a set of instructions for execution on a processing system that includes at least one processor and a machine learning engine, the execution causing a method to be carried out of determining a measure of relevance of a client-provided message to client-provided users, the method comprising:

• building a machine-learning model to predict the two or more dimensions of a multidimensional profile of a message of creative content, the building using postings on at least one respective media platform of each of a set of users and two or more dimensions of multidimensional profiles of the users of the set;
• receiving a message of creative content from a client;
• using the built machine-learning model to determine the two or more dimensions of a multidimensional signature of the received message;
• receiving from the client client-selected users;
• selecting from the client-selected users a set of client-selected users on whom behavioral footprint data is available;
• retrieving or determining the two or more dimensions of the subset multidimensional profiles of the set of client-selected users;
• determining the two or more dimensions of a multidimensional signature of the set of client-selected users;
• and
• comparing the two or more dimensions of the signatures of the set of client-selected users and of the received message to determine a measure of relevance of the message of creative content to the set of client-selected users.