Intuit, Inc.

James Rodney Jennings

Yao H Morin

Lei Pei

Christian A Rodriguez

Jyotiswarup Pai Raiturkar

Joseph B Cessna

Vi Joy Caro

Tia Krupa

Dorian Kersch

Danielle D'Agostino

Yantian Chu

Christian Anthony Rodriguez Mercado

Ellery Addington-White

Stefanos L Missailidis

Evan Francis

Yao Morin

Systems and methods for generating recommended offers are disclosed. An example method may be performed by one or more processors of a recommendation system and include correlating attributes of users with attributes of offers based on historical data associated with the users and offers, training a machine learning model to predict a user's interest in an offer based on the correlating, obtaining current user data, obtaining current offer data, providing the current user data and the current offer data to the trained machine learning model, generating, using the trained machine learning model, a predicted level of interest that the current user has in each respective current offer of the number of current offers, identifying, among the number of current offers, at least one current offer having a predicted level of interest for the current user greater than a value, and generating one or more recommended offers for the current user.

Method and system for using machine learning techniques to identify and recommend relevant offers

Certain aspects of the present disclosure provide techniques for ranking users based on similarity. One example method generally includes receiving a request to view a list of users similar to a user based on personal information and receiving user data generated based on attribute data and financial data associated with the user comprising value data of the user. The method further includes obtaining clusters comprising value data of a plurality of users based on mapped data of the plurality of users and determining, by comparing the value data of the user data with the value data of the clusters, a cluster of vectors similar to the user data. The method further includes generating, based on a list of user identifiers using a distance function, a ranked list of similar users and transmitting the ranked list of similar users to a user device for display.

Ranking similar users based on values and personal journeys

Systems and methods are provided that, in some embodiments that extract user data from at least one data warehouse. The user data is sorted within each dimension, and partitions each dimension into bins. Clusters are defined as each bin that includes user data for a number of users that exceeds a threshold. Clusters are determined for every combination of dimensions. Each combination of clusters that exceed the threshold is defined as clusters that are formed from multiple dimensions. All clusters and other clusters are stored into a cluster definition table. The clusters are used to analyze the profile of specific users.

Systems and methods for intelligently grouping financial product users into cohesive cohorts

A method for measuring financial health involves aggregating financial data from different data sources into a single financial data warehouse; extracting multiple customer financial characteristics from the financial data warehouse to build multiple financial profiles for a plurality of known customers; training a predictive machine learning model using the multiple financial profiles of multiple known customers to obtain a trained model; generating a grade mapping table using the trained model and a pre-defined business rule; applying the trained model to the financial data of a user to obtain a financial health score set; applying the grade mapping table to the financial data of the user to obtain a financial health grade set; and presenting the financial health grade set and the financial health score set to the user via a multimedia user interface.

Method and system for aggregating personal financial data to predict consumer financial health

User data from users/consumers is transformed into machine learning training data including historical offer attribute model training data, historical offer performance model training data, and user attribute model training data associated with two or more users/consumers, and, in some cases, millions, tens of millions, or hundreds of millions or more, users/consumers. The machine learning training data is then used to train one or more offer/attribute matching models in an offline training environment. A given current user's data and current offer data are then provided as input data to the offer/attribute matching models in an online runtime/execution environment to identify current offers predicted to have a threshold level of user interest. Recommendation data representing these offers is then provided to the user and the current user's actions with respect to the recommended offers is monitored and used as online training data.

Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Method and system for using machine learning techniques to make highly relevant and de-duplicated offer recommendations

A method for obtaining a comparison criterion selected by a first user. The method further includes comparing the comparison criterion of the first user to a second user to determine a similarity metric between the first user and the second user. The method further includes adjusting the learning algorithm to create a correlation between the comparison criterion of the first user and a second user to generate an adjusted learning algorithm. The method further includes selecting, a custom tip based on the correlation, a credit score of the first user, and a credit score of the second user. The method further includes the custom tip and a comparison of the credit score of the first user with the credit score of the second user based on the similarity metric.

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Mountain View, CA, United States of America

James Rodney Jennings