Gender attribute assignment using a multimodal neural graph

The method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:

• receiving from an item catalog database a respective item description and respective attribute values for each item of a set of items, wherein a gender classification of the respective attribute values for the each item of the set of items is either labeled or unlabeled;
• generating text embeddings using a text embedding model to represent the respective item description and the respective attribute values for the each item of the set of items;
• generating a graph of the set of items from the item catalog database connected by a set of edges, wherein each pair of items of the set of items that is connected by a respective edge of the set of edges in the graph has been viewed together in one or more respective sessions, the respective edge comprises a weight comprising a co-view count, and the set of edges comprises (a) one or more unlabeled-unlabeled edges, (b) one or more labeled-unlabeled edges, and (c) one or more labeled-labeled edges;
• training the text embedding model and a machine learning model using a neural loss function based on the graph;
• and
• automatically determining, based on the machine learning model, as trained, a gender label for each first item of the set of items in which the gender classification is unlabeled and in which a respective quantity of respective attribute values for the each first item is at least a predetermined threshold.