System and method for noise-resistant complementary item recommendation

A computer-implemented method, comprising:

• generating a trained model based on transaction data identifying a plurality of transactions of a plurality of users;
• representing each item of a set of items as an item embedding in an embedding space based on the trained model, wherein the item embedding is a Gaussian distribution with a mean vector and a non-zero covariance matrix;
• determining an anchor item to be displayed to a user via a user interface executed on a user device of the user;
• representing the anchor item as an anchor embedding in the embedding space based on the trained model, wherein the anchor embedding is a Gaussian distribution with an anchor mean vector and an anchor non-zero covariance matrix;
• computing a complementarity score for each item of the set of items, based on a distance between the mean vector of the item and the anchor mean vector;
• generating a ranking for the set of items based on their respective complementarity scores;
• selecting a plurality of top items in the set of items based on the ranking as recommended complementary items;
• and
• transmitting information about the recommended complementary items to the user device to be displayed with the anchor item on the user interface.

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations comprising:

• generating a trained model based on transaction data identifying a plurality of transactions of a plurality of users;
• representing each item of a set of items as an item embedding in an embedding space based on the trained model, wherein the item embedding is a Gaussian distribution with a mean vector and a non-zero covariance matrix;
• determining an anchor item to be displayed to a user via a user interface executed on a user device of the user;
• representing the anchor item as an anchor embedding in the embedding space based on the trained model, wherein the anchor embedding is a Gaussian distribution with an anchor mean vector and an anchor non-zero covariance matrix;
• computing a complementarity score for each item of the set of items, based on a distance between the mean vector of the item and the anchor mean vector;
• generating a ranking for the set of items based on their respective complementarity scores;
• selecting a plurality of top items in the set of items based on the ranking as recommended complementary items;
• and
• transmitting information about the recommended complementary items to the user device to be displayed with the anchor item on the user interface.