Abstract: A system and method for generating personalized content for a user device. In one example, a system can cause a computing device to display a first plurality of search results on a user interface of a user device based at least in part on a first request. Clickstream data can be identifier from the user interface displayed on the user device. The clickstream data comprises a user-specified sort of the first plurality of search results on the user interface. A sorting habit for the user can be determined based at least in part on the user-specified sort of the first plurality of search results on the user interface. The computing device can display on the user interface a second plurality of search results. The second plurality of search results are sorted for display based at least in part on the sorting habit for the user.

Abstract: A system and method for generating personalized content for a user device. In one example, a system can cause a computing device to generate a merchant score for a plurality of merchants based at least in part on merchant statistics and display a user interface on a user device of a user. The user interface includes a subset of the merchants that have been identified for a user based at least in part on the merchant score. Clickstream data can be received from the user interface displayed on the user device. An updated merchant score can be generated based at least in part on the user preference. A sorting habit for the user is determined based at least in part on a user-specified sort received from the user interface. A modified subset of the merchants is displayed on the user device based on the sorting habit.

Abstract: The present disclosure includes a system, method, and article of manufacture for generating an entity graph. The method may comprise determining a relationship between a first entity and a second entity based upon internal data, external data, and/or online data associated with the first entity, and generating the entity graph comprising at least two nodes and an edge connecting the at least two nodes. The method may further comprise, in various embodiments, tailoring marketing to the first entity based upon the entity graph, detecting fraud against the first entity based upon the entity graph, periodically updating the entity graph based upon new internal data and new online data, and/or adjusting the edge based upon a change in the relationship between the first entity and the second entity.

Abstract: The systems and methods described herein may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input at least one of: a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, a share of wallet, and/or the like.

Abstract: The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

Abstract: The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

Abstract: A system and method for generating personalized content for a user device. In one example, a system can cause a computing device to generate a merchant score for a plurality of merchants based at least in part on merchant statistics and display a user interface on a user device of a user. The user interface includes a subset of the merchants that have been identified for a user based at least in part on the merchant score. Clickstream data can be received from the user interface displayed on the user device. An updated merchant score can be generated based at least in part on the user preference. A sorting habit for the user is determined based at least in part on a user-specified sort received from the user interface. A modified subset of the merchants is displayed on the user device based on the sorting habit.

Abstract: A system may generate a first transaction history from a plurality of records of charge. The system may select a plurality of merchants based on the plurality of records of charge, and generate a first user profile in response to the first transaction history and the plurality of merchants. The first user profile may comprise a merchant score for a merchant appearing in the first transaction history. The first transaction history may include transaction account issuer data. The system may receive a request for content that identifies a user account associated with the first user profile. The system may further generate the content based on the merchant score in response to the request for the content. The request may be automatically generated by the application or may be in response to an action by the first user.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: The systems are generally configured to provide rewards. The systems are capable of syncing transaction accounts with one or more reward programs and one or more channels. Moreover, the systems are capable dynamically provisioning rewards in one or more subsets to a rewards program to distribute high, medium and low value rewards to a reward program throughout the duration of the program.

Abstract: At least some embodiments are directed to a system that receives data generated when point of sale terminals are used. The point of sale terminals are associated with multiple merchants. The system generates a quantitative data from the received data. The system computes a quality of service score of a first merchant based on the quantitative data. The system generates an item data structure storing values that correlate multiple users with multiple merchants at least based on the quality of service score. The system determines users that are shared by multiple merchants and computes a customer relevance value associated with the user and the first merchant. The system outputs a user-specific recommendation associated with a second merchant to a user computing device, wherein the user-specific recommendation is computed at least in part based on the customer relevance value associated with the first merchant.

Abstract: The systems are generally configured to provide rewards. The systems are capable of syncing transaction accounts with one or more reward programs and one or more channels. Moreover, the systems are capable dynamically provisioning rewards in one or more subsets to a rewards program to distribute high, medium and low value rewards to a reward program throughout the duration of the program.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: A system may generate a first transaction history from a plurality of records of charge. The system may select a plurality of merchants based on the plurality of records of charge, and generate a first user profile in response to the first transaction history and the plurality of merchants. The first user profile may comprise a merchant score for a merchant appearing in the first transaction history. The first transaction history may include transaction account issuer data. The system may receive a request for content that identifies a user account associated with the first user profile. The system may further generate the content based on the merchant score in response to the request for the content. The request may be automatically generated by the application or may be in response to an action by the first user.

Abstract: A system may generate a first transaction history from a plurality of records of charge. The system may select a plurality of merchants based on the plurality of records of charge, and generate a first user profile in response to the first transaction history and the plurality of merchants. The first user profile may comprise a merchant score for a merchant appearing in the first transaction history. The first transaction history may include transaction account issuer data. The system may receive a request for content that identifies a user account associated with the first user profile. The system may further generate the content based on the merchant score in response to the request for the content. The request may be automatically generated by the application or may be in response to an action by the first user.

Abstract: The systems and methods described herein may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input at least one of: a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, a share of wallet, and/or the like.

Abstract: The systems and methods described herein may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input at least one of: a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, a share of wallet, and/or the like.

Abstract: The present disclosure includes a system, method, and article of manufacture for generating an entity graph. The method may comprise determining a relationship between a first entity and a second entity based upon internal data, external data, and/or online data associated with the first entity, and generating the entity graph comprising at least two nodes and an edge connecting the at least two nodes. The method may further comprise, in various embodiments, tailoring marketing to the first entity based upon the entity graph, detecting fraud against the first entity based upon the entity graph, periodically updating the entity graph based upon new internal data and new online data, and/or adjusting the edge based upon a change in the relationship between the first entity and the second entity.

Abstract: The present disclosure includes a system, method, and article of manufacture for generating an entity graph. The method may comprise determining a relationship between a first entity and a second entity based upon internal data, external data, and/or online data associated with the first entity, and generating the entity graph comprising at least two nodes and an edge connecting the at least two nodes. The method may further comprise, in various embodiments, tailoring marketing to the first entity based upon the entity graph, detecting fraud against the first entity based upon the entity graph, periodically updating the entity graph based upon new internal data and new online data, and/or adjusting the edge based upon a change in the relationship between the first entity and the second entity.

Abstract: The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.