US 11,531,916 B2Granted

System and method for obtaining recommendations using scalable cross-domain collaborative filtering

AssigneePaypal, Inc.

InventorsDinesh Kumar, Yuanyuan Pan, Prashant Gaurav, Fransisco Kurniadi, Kevin Ward, Yue Xin, Krishnakumar Govindarajalu, Kimberly Kidney, Tao Sun

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Abstract

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.

Claims20

Dependent claims

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.

7 more elements

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.

5 more elements

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.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/213,346Filed 7 Dec 2018
Published: US 2020/0184357 A111 Jun 2020
Granted: 20 Dec 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification13 codes

G06N 20/20
Machine learning: ensemble learning
G06N 5/048
Inference or reasoning models: fuzzy inferencing
G06N 7/01
Computing arrangements based on specific mathematical models: probabilistic graphical models, e.g. probabilistic networks
G06Q 10/42
Business processes related to social networking or social networking services: determination of affinities or common interests between users