Systems and methods for generating recommendations using neural network and machine learning techniques

1. A system for recommending complementary items comprising:

one or more processors; and

one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and perform:

receiving an input identifying an anchor item;

determining, using a quadruplet network associated with a neural network architecture, one or more item categories corresponding to the complementary items associated with the anchor item;

generating, using a ranking network associated with the neural network architecture, scores for the complementary items included in the one or more item categories;

generating, using the ranking network associated with the neural network architecture, first ranking results for the complementary items based, at least in part, on the scores; and

selecting one or more of the complementary items to be displayed based, at least in part, on the first ranking results.

2. The system of claim 1 , wherein the computing instructions are further configured to run on the one or more processors and perform:

determining whether a user profile is accessible for an individual who selected the anchor item;

in response to determining that the user profile for the individual is accessible, updating the scores for the complementary items using a re-ranking network associated with the neural network architecture; and

updating the first ranking results to create second ranking results for the complementary items based on the scores that are updated by the re-ranking network.

3. The system of claim 2 , wherein the one or more complementary items are selected to be displayed based, at least in part, on the second ranking results updated by the re-ranking network.

4. The system of claim 2 , wherein generating the second ranking results comprises:

generating, using the re-ranking network of the neural network architecture, a user profile embedding corresponding to the user profile;

generating, using the re-ranking network of the neural network architecture, item profile embeddings for the complementary items associated with the second ranking results; and

using the user profile embedding and item profile embeddings to update the scores.

5. The system of claim 1 , wherein generating the first ranking results comprises:

generating, using a text encoder associated with the neural network architecture, dense features comprising title embeddings and category embeddings corresponding to the complementary items; and

generating the first ranking results based, at least in part, on the dense features.

6. The system of claim 5 , wherein generating the first ranking results further comprises:

generating categorical features associated with the complementary items;

generating continuous features associated with the complementary items; and

generating the first ranking results based, at least in part, on the dense features, the categorical features, and the continuous features.

7. The system of claim 1 , wherein the anchor item and the one or more complementary items are accessible via an electronic platform over a network.

8. The system of claim 1 , wherein the computing instructions are further configured to run on the one or more processors and perform:

determining whether a user profile is accessible for an individual who selected the anchor item; and

in response to determining that the user profile for the individual is accessible, customizing the first ranking results based, at least in part, on the user profile.

9. A method for recommending complementary items implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising:

receiving an input identifying an anchor item;

determining, using a quadruplet network associated with a neural network architecture, one or more item categories corresponding to the complementary items associated with the anchor item;

generating, using a ranking network associated with the neural network architecture, scores for the complementary items included in the one or more item categories;

generating, using the ranking network associated with the neural network architecture, first ranking results for the complementary items based, at least in part, on the scores; and

selecting one or more of the complementary items to be displayed based, at least in part, on the first ranking results.

10. The method of claim 9 further comprising:

determining whether a user profile is accessible for an individual who selected the anchor item;

in response to determining that the user profile for the individual is accessible, updating the scores for the complementary items using a re-ranking network associated with the neural network architecture; and

updating the first ranking results to create second ranking results for the complementary items based on the scores that are updated by the re-ranking network.

11. The method of claim 10 , wherein the one or more complementary items are selected to be displayed based, at least in part, on the second ranking results updated by the re-ranking network.

12. The method of claim 10 , wherein generating the second ranking results comprises:

generating, using the re-ranking network of the neural network architecture, a user profile embedding corresponding to the user profile;

generating, using the re-ranking network of the neural network architecture, item profile embeddings for the complementary items associated with the second ranking results; and

using the user profile embedding and item profile embeddings to update the scores.

13. The method of claim 9 , wherein generating the first ranking results comprises:

generating, using a text encoder associated with the neural network architecture, dense features comprising title embeddings and category embeddings corresponding to the complementary items; and

generating the first ranking results based, at least in part, on the dense features.

14. The method of claim 13 , wherein generating the first ranking results further comprises:

generating categorical features associated with the complementary items;

generating continuous features associated with the complementary items; and

generating the first ranking results based, at least in part, on the dense features, the categorical features, and the continuous features.

15. The method of claim 9 , wherein the anchor item and the one or more complementary items are accessible via an electronic platform over a network.

16. The method of claim 9 further comprising:

determining whether a user profile is accessible for an individual who selected the anchor item; and

in response to determining that the user profile for the individual is accessible, customizing the first ranking results based, at least in part, on the user profile.

17. A computer program product for recommending complementary items, the computer program product comprising a non-transitory computer-readable medium including instructions for causing a computer to:

receive an input identifying an anchor item;

determine, using a quadruplet network associated with a neural network architecture, one or more item categories corresponding to the complementary items associated with the anchor item;

generate, using a ranking network associated with the neural network architecture, scores for the complementary items included in the one or more item categories;

generate, using the ranking network associated with the neural network architecture, first ranking results for the complementary items based, at least in part, on the scores; and

select one or more of the complementary items to be displayed based, at least in part, on the first ranking results.

18. The computer program product of claim 17 , wherein the instructions further cause the computer to:

determine whether a user profile is accessible for an individual who selected the anchor item;

in response to determining that the user profile for the individual is accessible, update the scores for the complementary items using a re-ranking network associated with the neural network architecture; and

update the first ranking results to create second ranking results for the complementary items based on the scores that are updated by the re-ranking network.

19. The computer program product of claim 18 , wherein the one or more complementary items are selected to be displayed based, at least in part, on the second ranking results updated by the re-ranking network.

20. The computer program product of claim 18 , wherein generating the second ranking results comprises:

generating, using the re-ranking network of the neural network architecture, a user profile embedding corresponding to the user profile;

generating, using the re-ranking network of the neural network architecture, item profile embeddings for the complementary items associated with the second ranking results; and

using the user profile embedding and item profile embeddings to update the scores.