Machine learning techniques for assessing interfaces

A method for assessing interfaces based on analytics using machine learning models, the method comprising:

• receiving a request for evaluating a user interface based on a plurality of parameters, wherein the request comprises (1) a representation of the user interface and (2) one or more user-defined target values for the plurality of parameters;
• identifying, within the representation, a plurality of elements of the user interface and one or more structural properties of each element, wherein each structural property controls presentation of a corresponding element;
• generating, for each element, (1) a corresponding composite embedding representing corresponding one or more structural properties of each element and (2) a corresponding content embedding representing content of each element;
• generating a graph representation comprising a plurality of nodes and a plurality of edges, wherein each node corresponds to an element of the plurality of elements, wherein each node is assigned a vector representation defined by the corresponding composite embedding and the corresponding content embedding for that element, and wherein each edge comprises one or more values representing a relative distance between two elements of the plurality of elements;
• generating, based on the graph representation, a graph embedding using an embedding model trained to transform graph representations into embeddings;
• inputting the graph embedding into a parameter analysis machine learning model to obtain a corresponding predicted value for each of the plurality of parameters, wherein the parameter analysis machine learning model is trained to predict values for parameters based on graph embeddings;
• and
• transmitting, to a remote device, a corresponding predicted value for each of the plurality of parameters.

One or more non-transitory, computer-readable media comprising instructions recorded thereon that, when executed by one or more processors, cause operations for assessing interfaces using machine learning models, comprising:

• receiving a request for evaluating a user interface based on a plurality of parameters, wherein the request comprises (1) a representation of the user interface and (2) one or more user-defined target values for the plurality of parameters;
• identifying, within the representation, a plurality of elements of the user interface and one or more structural properties of each element, wherein each structural property controls presentation of a corresponding element;
• generating, for each element, (1) a corresponding composite embedding representing corresponding one or more structural properties of each element and (2) a corresponding content embedding representing content of each element;
• generating a graph representation comprising a plurality of nodes and a plurality of edges, wherein each node corresponds to an element of the plurality of elements, wherein each node is assigned a vector representation defined by the corresponding composite embedding and the corresponding content embedding for that element, and wherein each edge comprises one or more values representing a relative distance between two elements of the plurality of elements;
• generating, based on the graph representation, a graph embedding using an embedding model trained to transform graph representations into embeddings;
• and
• inputting the graph embedding into a parameter analysis machine learning model to obtain a corresponding predicted value for each of the plurality of parameters, wherein the parameter analysis machine learning model is trained to predict values for parameters based on graph embeddings.