Personalized item recommendations through large-scale deep-embedding architecture with real-time inferencing

A 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:

• training two sets of item embeddings for items in an item catalog and a set of user embeddings for users, using a triple embeddings model, with triplets, wherein the triplets each comprise a respective first user of the users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket;
• generating an approximate nearest neighbor index for the two sets of item embeddings;
• receiving a basket comprising basket items selected by a user from the item catalog;
• grouping the basket items of the basket into categories based on a respective item category of each of the basket items;
• randomly sampling a respective anchor item from each of the categories;
• generating a respective list of complementary items for the respective anchor item for the each of the categories based on a respective lookup call to the approximate nearest neighbor index using a query vector associated with the user and the respective anchor item, wherein the query vector is generated for the user and the respective anchor item using the two sets of item embeddings and the set of user embeddings;
• building a list of personalized recommended items for the user based on the respective lists of the complementary items for the categories;
• and
• sending instructions to display, to the user on a user interface of a user device, at least a portion of the list of personalized recommended items.