Systems and methods for generating recommendations using a corpus of data

A computer-based method for predicting preferences, the method implemented using a recommender computer device including at least one processor coupled to a memory device, the method comprising steps performed by the at least one processor comprising:

• accessing a database storing communications routed by a payment card network between a plurality of acquirer computing devices and a plurality of issuer computing devices, each of the communications formatted according to a standard for exchange of financial transaction data and settlement of funds between financial institutions that are customers of the payment card network, each of the communications corresponding to a transaction between a respective one of a plurality of merchants and a respective one of a plurality of consumers, each of the communications including data identifying the respective merchant and an account of the respective consumer;
• generating, in the memory device for a list of merchants included in the plurality of merchants, an array of values (i,j) corresponding to a merchant correspondence matrix, wherein the list comprises an ordered sequence of merchants numbered 1 to n, i and j are indices, the index i ranges from 1 to n, and the index j ranges from 1 to n, said generating comprising:
• searching the data identifying the respective merchant in the communications to identify transactions associated with the merchants in the list of merchants;
• identifying, from the communications associated with the identified transactions, the accounts that transacted at each merchant in the list of merchants;
• populating, in the memory device, each of the values (i,j) of the array based on a number of co-visits of merchant i and merchant j, wherein each co-visit is defined as a respective one of the identified accounts having at least one of the identified transactions at merchant i and at least one of the identified transactions at merchant j;
• multiplying the merchant correspondence matrix and a uniform preference vector to obtain a first results vector, the uniform preference vector having n equally weighted elements;
• determining an initial rank of each merchant in the list based on the values in the first results vector,
• receiving, from a client computing device via the Internet, a registration from a first consumer of the plurality of consumers, the first consumer associated with a first account;
• searching the data identifying the respective account in the communications to identify transactions associated with the first account;
• identifying, from the communications associated with the first account, each merchant in the list of merchants at which the first account transacted;
• generating a user preference vector for the first account, the user preference vector comprising n elements, wherein elements corresponding in sequence to one of the visited merchants are weighted heavier than the elements of the user preference vector that do not correspond to one of the visited merchants;
• receiving, from the client computing device via the Internet, a query from the first consumer for a recommendation of a merchant;
• generating a second results vector, comprising multiplying, by the at least one processor, the merchant correspondence matrix and the user preference vector;
• determining an updated rank of each merchant in the list based on the values in the second results vector;
• predicting a ranked preference of the first consumer for the merchants in the list of merchants based on a change in rank of merchants in the list between the initial rank and the updated rank;
• and
• identifying, to the client computing device via the Internet, one or more merchants based on the predicted ranked preference.

A preference predictor system comprising at least one processor coupled to a memory device, said memory device including instructions executable to cause the at least one processor to perform steps including:

• accessing a database storing communications routed by a payment card network between a plurality of acquirer computing devices and a plurality of issuer computing devices, each of the communications formatted according to a standard for exchange of financial transaction data and settlement of funds between financial institutions that are customers of the payment card network, each of the communications corresponding to a transaction between a respective one of a plurality of merchants and a respective one of a plurality of consumers, each of the communications including data identifying the respective merchant and an account of the respective consumer;
• generating, in the memory device for a list of merchants included in the plurality of merchants, an array of values (i,j) corresponding to a merchant correspondence matrix, wherein the list comprises an ordered sequence of merchants numbered 1 to n, i and j are indices, the index i ranges from 1 to n, and the index j ranges from 1 to n, the generating comprising:
• searching the data identifying the respective merchant in the communications to identify transactions associated with the merchants in the list of merchants;
• identifying, from the communications associated with the identified transactions, the accounts that transacted at each merchant in the list of merchants;
• populating, in the memory device, each of the values (i,j) of the array based on a number of co-visits of merchant i and merchant j, wherein each co-visit is defined as a respective one of the identified accounts having at least one of the identified transactions at merchant i and at least one of the identified transactions at merchant j;
• multiplying the merchant correspondence matrix and a uniform preference vector to obtain a first results vector, the uniform preference vector having n equally weighted elements;
• determining an initial rank of each merchant in the list based on the values in the first results vector,
• receiving, from a client computing device via the Internet, a registration from a first consumer of the plurality of consumers, the first consumer associated with a first account;
• searching the data identifying the respective account in the communications to identify transactions associated with the first account;
• identifying, from the communications associated with the first account, each merchant in the list of merchants at which the first account transacted;
• generating a user preference vector for the first account, the user preference vector comprising n elements, wherein elements corresponding in sequence to one of the visited merchants are weighted heavier than the elements of the user preference vector that do not correspond to one of the visited merchants;
• receiving, from the client computing device via the Internet, a query from the first consumer for a recommendation of a merchant;
• generating a second results vector, comprising multiplying, by the at least one processor, the merchant correspondence matrix and the user preference vector;
• determining an updated rank of each merchant in the list based on the values in the second results vector;
• predicting a ranked preference of the first consumer for the merchants in the list of merchants based on a change in rank of merchants in the list between the initial rank and the updated rank;
• and
• identifying, to the client computing device via the Internet, one or more merchants based on the predicted ranked preference.

One or more non-transitory computer-readable storage media having computer-executable instructions embodied thereon for execution by at least one processor of a preference predictor system, the at least one processor in communication with a memory device, the computer-executable instructions being executable to cause the at least one processor to perform steps including:

• accessing a database storing communications routed by a payment card network between a plurality of acquirer computing devices and a plurality of issuer computing devices, each of the communications formatted according to a standard for exchange of financial transaction data and settlement of funds between financial institutions that are customers of the payment card network, each of the communications corresponding to a transaction between a respective one of a plurality of merchants and a respective one of a plurality of consumers, each of the communications including data identifying the respective merchant and an account of the respective consumer;
• generating, in the memory device for a list of merchants included in the plurality of merchants, an array of values (i,j) corresponding to a merchant correspondence matrix, wherein the list comprises an ordered sequence of merchants numbered 1 to n, i and j are indices, the index i ranges from 1 to n, and the index j ranges from 1 to n, said generating comprising:
• searching the data identifying the respective merchant in the communications to identify transactions associated with the merchants in the list of merchants;
• identifying, from the communications associated with the identified transactions, the accounts that transacted at each merchant in the list of merchants;
• populating, in the memory device, each of the values (i,j) of the array based on a number of co-visits of merchant i and merchant j, wherein each co-visit is defined as a respective one of the identified accounts having at least one of the identified transactions at merchant i and at least one of the identified transactions at merchant j;
• multiplying the merchant correspondence matrix and a uniform preference vector to obtain a first results vector, the uniform preference vector having n equally weighted elements;
• determining an initial rank of each merchant in the list based on the values in the first results vector,
• receiving, from a client computing device via the Internet, a registration from a first consumer of the plurality of consumers, the first consumer associated with a first account;
• searching the data identifying the respective account in the communications to identify transactions associated with the first account;
• identifying, from the communications associated with the first account, each merchant in the list of merchants at which the first account transacted;
• generating a user preference vector for the first account, the user preference vector comprising n elements, wherein elements corresponding in sequence to one of the visited merchants are weighted heavier than the elements of the user preference vector that do not correspond to one of the visited merchants;
• receiving, from the client computing device via the Internet, a query from the first consumer for a recommendation of a merchant;
• generating a second results vector, comprising multiplying, by the at least one processor, the merchant correspondence matrix and the user preference vector;
• determining an updated rank of each merchant in the list based on the values in the second results vector;
• predicting a ranked preference of the first consumer for the merchants in the list of merchants based on a change in rank of merchants in the list between the initial rank and the updated rank;
• and
• identifying, to the client computing device via the Internet, one or more merchants based on the predicted ranked preference.