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 recommendation models and a recommendation made based on the recommendation score determined. In another embodiment, the system is introduced that can re-train the recommendation model based on a feedback received in response to a recommendation made using on the recommendation score obtained.
1. A system, comprising:
a non-transitory memory storing instructions;
a processor configured to execute the instructions to cause the system to:
in response to a notification received that a user is at a checkout, retrieve, from a customer engagement platform, user characterization information;
select one or more recommendation models to use for determining a recommendation to present to the user, based on the user characterization information,
the selected one or more recommendation models including one or both of a random walk model or a clustering model; and
the clustering model selected when the user characterization information includes user profile information;
compute, using the selected one or more recommendation models, at least one recommendation score;
analyze the at least one recommendation score to determine the recommendation to present to the user;
transmit, over a communication network and to a computing device associated with the user, the determined recommendation that causes the determined recommendation to be displayed on the computing device to the user;
receive, via the computing device and in response to the determined recommendation displayed to the user, feedback from the user; and
re-train the selected one or more recommendation models based on the received feedback in a recommendation model-training feedback loop including the selected one or more recommendation models and the received feedback.
2. The system of claim 1 , wherein:
the one or more recommendation models include a plurality of recommendation models; and
the at least one recommendation score is computed using the plurality of recommendation models and an ensemble model.
3. The system of claim 2 , wherein the ensemble model includes a decision tree model.
4. The system of claim 2 , wherein the ensemble model uses a plurality of recommendation scores generated by the plurality of recommendation models to compute the at least one recommendation score.
5. The system of claim 1 , wherein the random walk model is selected when the user characterization information includes a combination of user profile information and peer-to-peer transactions information.
6. The system of claim 1 , wherein the selected one or more recommendation models comprises at least two recommendation models that further include a cross-domain filtering model.
7. The system of claim 1 , wherein the cross-domain filtering model is selected when the user characterization information includes a combination of user profile information, peer-to-peer transactions information, and cross-domain transactional information.
8. A method performed by one or more processors, comprising:
in response to a notification received that a user is at a checkout, retrieving, from a customer engagement platform, user characterization information;
selecting one or more recommendation models to use for determining a recommendation to present to the user, based on the user characterization information,
the selected one or more recommendation models including one or both of a random walk model or a clustering model;
the random walk model is selected when the user characterization information includes a combination of user profile information and peer-to-peer transactions information; and
the clustering model selected when the user characterization information includes user profile information;
computing, using the selected one or more recommendation models, at least one recommendation score;
analyzing the at least one recommendation score to determine the recommendation to present to the user;
transmitting, over a communication network and to a computing device associated with the user, the determined recommendation that causes the determined recommendation to be presented the user through a display of the computing device;
receiving, via the computing device and in response to the determined recommendation presented to the user, feedback from the user; and
re-training the selected one or more recommendation models based on the received feedback in a recommendation model-training feedback loop including the selected one or more recommendation models and the received feedback.
9. The method of claim 8 , wherein:
the one or more recommendation models include a plurality of recommendation models; and
the at least one recommendation score is computed using the plurality of recommendation models and an ensemble model.
10. The method of claim 9 , wherein the ensemble model includes a decision tree model.
11. The method of claim 9 , wherein the ensemble model uses a plurality of recommendation scores generated by the plurality of recommendation models to compute the at least one recommendation score.
12. The method of claim 8 , wherein the selected one or more recommendation models comprises at least two recommendation models that further include a cross-domain filtering model.
13. The method of claim 8 , wherein the cross-domain filtering model is selected when the user characterization information includes a combination of user profile information, peer-to-peer transactions information, and cross-domain transactional information.
14. A non-transitory machine-readable medium having instructions stored thereon, the instructions executable to cause performance of operations comprising:
in response to a notification received that a user is at a checkout, retrieving, from a customer engagement platform, user characterization information;
selecting one or more recommendation models to use for determining a recommendation to present to the user, based on the user characterization information,
the selected one or more recommendation models including one or both of a random walk model or a clustering model; and
the clustering model selected when the user characterization information includes user profile information;
computing, using the selected one or more recommendation models, at least one recommendation score;
analyzing the at least one recommendation score to determine the recommendation to present to the user;
transmitting, over a communication network and to a computing device associated with the user, the determined recommendation to cause the determined recommendation to be presented to the user via the computing device;
receiving, via the computing device and in response to the determined recommendation presented to the user, feedback from the user; and
re-training the selected one or more recommendation models based on the received feedback in a recommendation model-training feedback loop including the selected one or more recommendation models and the received feedback.
15. The non-transitory machine-readable medium of claim 14 , wherein the one or more recommendation models include a plurality of recommendation models; and
the at least one recommendation score is computed using the plurality of recommendation models and an ensemble model.
16. The non-transitory machine-readable medium of claim 15 , wherein the ensemble model includes a decision tree model and uses a plurality of recommendation scores generated by the plurality of recommendation models to compute the at least one recommendation score.
17. The non-transitory machine-readable medium of claim 14 , wherein the random walk model is selected when the user characterization information includes a combination of user profile information and peer-to-peer transactions information.
18. The non-transitory machine-readable medium of claim 14 , wherein the selected one or more recommendation models comprises at least two recommendation models that further include a cross-domain filtering model.
19. The non-transitory machine-readable medium of claim 14 , wherein the cross-domain filtering model is selected when the user characterization information includes a combination of user profile information, peer-to-peer transactions information, and cross-domain transactional information.