System and method for obtaining recommendations using scalable cross-domain collaborative filtering
Aspects of the present disclosure involve systems, methods, devices, and the like for presenting a recommendation. In one embodiment, a system is introduced that includes a plurality of models for obtaining a recommendation score. The recommendation score may be obtained using one or more models which can include supervised and unsupervised learning as well as a combination of user information and transactions. In another embodiment, the system is introduced that can provide a total recommendation score and recommendation generated by an ensemble model whose input can include the one or more recommendation scores previously obtained.
1. A system, comprising:
a non-transitory memory storing instructions;
a processor configured to execute the instructions to cause the system to:
receive, via a network from a merchant device, payment information for a transaction between a user and a merchant associated with the merchant device, based on an interaction by the user with a payment application associated with the merchant;
retrieve, from a database, user information for the user and cross-domain information for the merchant and other entities associated with a service provider;
determine, using a specific algorithm-based model with a first part of the user information retrieved from the database, a first recommendation score representing a first correlation between the user and the other entities;
determine, using a cross-domain collaborative filtering model with a second part of the user information and the cross-domain information retrieved from the database, a second recommendation score representing a second correlation between the user and the other entities;
apply the first and second recommendation scores as inputs to train an ensemble machine learning model to determine correlations between the user information and the cross-domain information using at least one of the specific algorithm-based model or the cross-domain collaborative filtering model;
determine, using the trained ensemble machine learning model, a total recommendation score; and
present, via a graphical user interface of the payment application, a recommendation for a possible transaction between the user and at least one of the other entities, based on the total recommendation score.
2. The system of claim 1 , wherein the specific algorithm-based model is a random walk model, and the first recommendation score is obtained using at least one of the random walk model or a clustering model.
3. The system of claim 2 , wherein the random walk model uses a combination of user profile information and peer-to-peer transactions from the user information to determine the first recommendation score.
4. The system of claim 2 , wherein the clustering model uses profile information from the user information retrieved to determine the first recommendation score.
5. The system of claim 1 , wherein the second recommendation score is obtained using a combination of the specific algorithm-based model and the cross-domain collaborative filtering model.
6. The system of claim 1 , wherein the cross-domain collaborative filtering model uses a combination of the user information and the cross-domain information to determine the second recommendation score.
7. The system of claim 1 , wherein the ensemble machine learning model includes a decision tree model and uses one or more of the first and second recommendation scores to obtain the total recommendation score.
8. The system of claim 1 , wherein a total recommendation score is obtained using the ensemble machine learning model and a combination of the first recommendation score determined using the specific algorithm-based model, the second recommendation score determined using the cross-domain collaborative filtering model, and a third recommendation score determined using a clustering model.
9. A method, comprising:
receiving, via a network from a merchant device, payment information for a transaction between a user and a merchant associated with the merchant device, based on an interaction by the user with a payment application associated with the merchant;
retrieving, from a database, user information for the user and cross-domain information for the merchant and other entities associated with a service provider;
determining, using a specific algorithm-based model with a first part of the user information retrieved from the database, a first recommendation score representing a first correlation between the user and the other entities;
determining, using a cross-domain collaborative filtering model with a second part of the user information and the cross-domain information retrieved from the database, a second recommendation score representing a second correlation between the user and the other entities;
applying the first and second recommendation scores as inputs to train an ensemble machine learning model to determine correlations between the user information and the cross-domain information using at least one of the specific algorithm-based model or the cross-domain collaborative filtering model;
determining, using the trained ensemble machine learning model, a total recommendation score; and
presenting, via a graphical user interface of the payment application, a recommendation for a possible transaction between the user and at least one of the other entities, based on the total recommendation score.
10. The method of claim 9 , wherein the specific algorithm-based model is a random walk model, and the first recommendation score is obtained using at least one of the random walk model or a clustering model.
11. The method of claim 10 , wherein the random walk model uses a combination of user profile information and peer-to-peer transactions from the user information to determine the first recommendation score.
12. The method of claim 10 , wherein the clustering model uses profile information from the user information retrieved to determine the first recommendation score.
13. The method of claim 9 , wherein the second recommendation score is obtained using a combination of the specific algorithm-based model and the cross-domain collaborative filtering model.
14. The method of claim 9 , wherein the cross-domain collaborative filtering model uses a combination of the user information and the cross-domain information to determine the second recommendation score.
15. The method of claim 9 , wherein the ensemble machine learning model includes a decision tree model and uses one or more of the first and second recommendation scores to obtain the total recommendation score.
16. The method of claim 9 , wherein a total recommendation score is obtained using the ensemble machine learning model and a combination of the first recommendation score determined using the specific algorithm-based model, the second recommendation score determined using the cross-domain collaborative filtering model, and a third recommendation score determined using a clustering model.
17. A non-transitory machine-readable medium having instructions stored thereon, the instructions executable to cause performance of operations comprising:
receiving, via a network from a merchant device, payment information for a transaction between a user and a merchant associated with the merchant device, based on an interaction by the user with a payment application associated with the merchant;
retrieving, from a database, user information for the user and cross-domain information for the merchant and other entities associated with a service provider;
determining, using a specific algorithm-based model with a first part of the user information retrieved from the database, a first recommendation score representing a first correlation between the user and the other entities;
determining, using a cross-domain collaborative filtering model with a second part of the user information and the cross-domain information retrieved from the database, a second recommendation score representing a second correlation between the user and the other entities;
applying the first and second recommendation scores as inputs to train an ensemble machine learning model to determine correlations between the user information and the cross-domain information using at least one of the specific algorithm-based model or the cross-domain collaborative filtering model;
determining, using the trained ensemble machine learning model, a total recommendation score; and
presenting, via a graphical user interface of the payment application, a recommendation for a possible transaction between the user and at least one of the other entities, based on the total recommendation score.
18. The non-transitory machine-readable medium of claim 17 , wherein the specific algorithm-based model is a random walk model, and the first recommendation score is obtained using at least one of the random walk model or a clustering model.
19. The non-transitory machine-readable medium of claim 18 , wherein the random walk model uses a combination of user profile information and peer-to-peer transactions from the user information to determine the first recommendation score.
20. The non-transitory machine-readable medium of claim 17 , wherein a total recommendation score is obtained using the ensemble machine learning model and a combination of the first recommendation score, the second recommendation score, and a third recommendation score determined using a clustering model.