Methods and system for automatic population of item recommendations in retail website in response to item selections

A retail web server system configured to provide recommendations to a user interface accessible to one or more users, the retail web server system comprising:

• at least one processor;
• a memory subsystem including one or more memories, the memory subsystem storing instructions which, when executed by the at least one processor, cause the retail web server system to perform:
• during an application session, receiving, at a user interface, selections of a first item and a second item by a user;
• retrieving a first embedding generated for the first item;
• retrieving a second embedding for the second item;
• generating a first graph comprising a first node and a second node;
• assigning the first embedding to the first node;
• assigning the second embedding to the second node;
• predicting, using a graph neural network trained to predict a next item in a sequence, a third item based on the selections of the first item and the second item, wherein predicting, using the trained graph neural network, the third item comprises modifying, using an edge-order preserving aggregation layer and a shortcut graph attention layer, the first embedding and the second embedding,
• wherein using the edge-order preserving aggregation comprises preserving a selection order between the first item and the second item, and performing a first message passing to update the first embedding and the second embedding based on embeddings of neighboring nodes in the first graph;
• wherein using the shortcut graph attention layer comprises adding, to the first graph, shortcut edges between pairs of nodes and subsequently selected nodes without an edge, and performing a second message passing to update the first embedding and the second embedding based on embeddings of neighboring nodes in the first graph;
• after applying the edge-order preserving aggregation layer and the shortcut graph attention layer, generating a session embedding by combining the updated first embedding and the updated second embedding;
• searching for a nearest embedding to the session embedding in an embeddings space;
• during the application session, displaying, in the user interface, the third item, wherein the third item corresponds to the nearest embedding.

A method for providing session-based recommendations, the method comprising:

• generating a first embedding for a first item using a graph neural network trained to predict a next item in a sequence;
• generating a second embedding for a second item using the graph neural network;
• during an application session, receiving, at a user interface, selections of the first item and the second item by a user;
• generating a first graph comprising a first node and a second node;
• assigning the first embedding to the first node;
• assigning the second embedding to the second node;
• predicting, using the graph neural network, a third item based on the selections of the first item and the second item, wherein predicting, using the trained graph neural network, the third item comprises modifying, using an edge-order preserving aggregation layer and a shortcut graph attention layer, the first embedding and the second embedding,
• wherein using the edge-order preserving aggregation comprises assigning a selection order between the first item and the second item, and performing a first message passing to update the first embedding and the second embedding based on embeddings of neighboring nodes in the first graph;
• wherein using the shortcut graph attention layer comprises adding, to the first graph, a shortcut edge between the first node and a subsequently selected node, and performing a second message passing to update the first embedding and the second embedding based on embeddings of neighboring nodes in the first graph;
• after applying the edge-order preserving aggregation layer and the shortcut graph attention layer, generating a session embedding using the updated first embedding and the updated second embedding;
• searching for a nearest embedding to the session embedding in an embeddings space;
• and
• during the application session, displaying, in the user interface, the third item, wherein the third item corresponds to the nearest embedding.