Techniques for improved user experience prediction

A system comprising:

• one or more processors;
• and
• one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• receiving a sequence of web pages visited by a user;
• applying a machine learning model to (i) the sequence of web pages and (ii) a set of metrics data corresponding to the sequence of web pages, wherein applying the machine learning model includes
• generating one or more embeddings of web page identifiers associated with the sequence of web pages,
• determining, by a first hidden layer of the machine learning model and for each respective embedding of the one or more embeddings, a first modified embedding based on respective cross-effects associated with one or more other embeddings of the one or more embeddings,
• determining, by a second hidden layer of the machine learning model and for each respective first modified embedding, a second modified embedding based on the set of metrics data associated with a respective first modified embedding, and
• outputting a user experience value for each second modified embedding;
• and
• generating one or more data objects indicating one or more of the user experience values.

One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving a sequence of web pages visited by a user;
• applying a machine learning model to (i) the sequence of web pages and (ii) a set of metrics data corresponding to the sequence of web pages, wherein applying the machine learning model includes
• generating one or more embeddings of web page identifiers associated with the sequence of web pages,
• determining, by a first hidden layer of the machine learning model and for each respective embedding of the one or more embeddings, a first modified embedding based on respective cross-effects associated with one or more other embeddings of the one or more embeddings,
• determining, by a second hidden layer of the machine learning model and for each respective first modified embedding, a second modified embedding based on the set of metrics data associated with a respective first modified embedding, and
• outputting a user experience value for each second modified embedding;
• and
• generating one or more data objects indicating one or more of the user experience values.