Paypal, Inc.

Krishnakumar Govindarajalu

Yue Xin

Tao Sun

Kevin Ward

Fransisco Kurniadi

Dinesh Kumar

Prashant Gaurav

Yuanyuan Pan

Kimberly Kidney

Cynthia L O'Yang

Dharmendra Kumar

There are provided systems and methods for intelligent interface displays based on past data correlations. A service provider and/or entity may implement a machine learning engine to dynamically adjust data displayed in interface elements of an interface provided by the service provider or entity. The data may correspond to an amount for a voluntary payment to the entity by a user and may be based on correlating and processing past user transactions and received entity payments. Weights may be applied to the data to determine median, average, and/or quartile amounts based on goals set by the user, events occurring at the time of the voluntary payment, and/or time of year. Once the machine learning engine determines one or more amounts specific to the user's payment to the entity, the engine may adjust or output the interface having the user specific data for processing.

Intelligent interface displays based on past data correlations

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.

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.

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San Jose, CA, United States of America

Krishnakumar Govindarajalu