Artificial intelligence recommendations for matching content of one content type with content of another

A method comprising:

• accessing a first piece of content of a first content type and a second piece of content of a second content type;
• feeding the first piece of content into a first generative artificial intelligence (GAI) model, the first GAI model outputting a first embedding corresponding to the first piece of content, the first embedding being a representation of a meaning of the first piece of content;
• feeding the second piece of content into the first GAI model, the first GAI model outputting a second embedding corresponding to the second piece of content, the second embedding being a representation of a meaning of the second piece of content;
• accessing historical interaction information regarding pieces of content;
• feeding the first embedding and the second embedding into a machine learning model, the machine learning model outputting an effectiveness matching score indicative of effectiveness of matching the first piece of content the second piece of content with respect to a first metric based on the historical interaction information;
• and
• based on the effectiveness matching score of the first piece of content and the second piece of content, passing the first and second pieces of content into a second GAI model to generate a combination piece of content.

A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:

• accessing a first piece of content of a first content type and a second piece of accessing a first piece of content of a first content type and a second piece of content of a second content type;
• feeding the first piece of content into a first generative artificial intelligence (GAI) model, the first GAI model outputting a first embedding corresponding to the first piece of content, the first embedding being a representation of a meaning of the first piece of content;
• feeding the second piece of content into the first GAI model, the first GAI model outputting a second embedding corresponding to the second piece of content, the second embedding being a representation of a meaning of the second piece of content;
• accessing historical interaction information regarding pieces of content;
• feeding the first embedding and the second embedding into a machine learning model, the machine learning model outputting an effectiveness matching score indicative of effectiveness of matching the first piece of content the second piece of content with respect to a first metric based on the historical interaction information;
• and
• based on the effectiveness matching score of the first piece of content and the second piece of content, passing the first and second pieces of content into a second GAI model to generate a combination piece of content.