Asset quality management using multimodality

A system for evaluating content item metadata elements for use in a content selection graphical user interface (GUI), comprising:

• one or more memories;
• and
• at least one processor each coupled to at least one of the memories and configured to perform operations comprising:
• processing the content item metadata elements using a deep neural network (DNN) trained to generate quality labels or scores for each of the content item metadata elements, wherein training of the DNN with the at least one processor or another at least one processor comprises:
• processing training metadata elements using a large language model (LLM), thereby generating asset embeddings corresponding to the training metadata elements;
• computing similarity scores between pairs of the asset embeddings;
• labeling ones of the training metadata elements having asset embeddings with similarity scores less than a first score threshold or greater than a second score threshold with a first label and ones of the training metadata elements having asset embeddings with similarity scores between the first score threshold and the second score threshold with a second label;
• and
• training the DNN to perform the generation of the quality labels or scores, the training using supervised learning with the labeled training metadata elements.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations for evaluating content item metadata elements for use in a content selection graphical user interface (GUI), the operations comprising:

• processing the content item metadata elements using a deep neural network (DNN) trained to generate quality labels or scores for each of the content item metadata elements, wherein training of the DNN with the at least one computing device or another at least one computing device comprises:
• processing training metadata elements using a large language model (LLM), thereby generating asset embeddings corresponding to the training metadata elements;
• computing similarity scores between pairs of the asset embeddings;
• labeling ones of the training metadata elements having asset embeddings with similarity scores less than a first score threshold or greater than a second score threshold with a first label and ones of the training metadata elements having asset embeddings with similarity scores between the first score threshold and the second score threshold with a second label;
• and
• training the DNN to perform the generation of the quality labels or scores, the training using supervised learning with the labeled training metadata elements.