Systems and methods for adjusting transaction affinity information

A method for determining adjusted transaction affinity recommendation data performed by transaction affinity platform computing components that execute software stored in a memory, the method comprising:

• receiving transaction data associated with a set of transactions associated with a first account record;
• analyzing the transaction data to generate a transaction affinity data set;
• identifying a first pair of temporally related transactions from the transaction affinity data set, the first pair of temporally related transactions including a first transaction and a second transaction that occurred later in time than the first transaction;
• analyzing transaction affinity data associated with each transaction in the first pair of temporally related transactions to determine time gap data associated with the first transaction and the second transaction;
• generating trip transaction data based on the time gap data;
• generating transaction affinity relationship data based on the trip transaction data;
• generating, based on the transaction affinity relationship data, merchant recommendation data associated with a set of recommended merchants that have an affinity relationship with a target merchant, such that the merchant recommendation data is generated before a new transaction occurs after the second transaction and the merchant recommendation data identifies a first recommended merchant included in the set of recommended merchants;
• determining merchant parameter information for each merchant in the set of recommended merchants;
• receiving input parameter information associated with a set of parameters, the set of parameters associated with parameters included in the merchant parameter information;
• determining a parameter weight value for each parameter in the set of parameters based on the input parameter information;
• analyzing the parameter weight values and the merchant parameter information;
• determining an adjustment to the merchant recommendation data based on analyzing the parameter weight values and the merchant parameter information;
• and
• generating, based on the determined adjustment, adjusted merchant recommendation data that identifies a second recommended merchant in the set of recommended merchants in place of the first recommended merchant.

A non-transitory computer-readable medium storing instructions for wireless communication, the instructions comprising:

• one or more instructions that, when executed by one or more processors, cause the one or more processors to:
• receive transaction data associated with a set of transactions associated with a first account record;
• analyze the transaction data to generate a transaction affinity data set;
• identify a first pair of temporally related transactions from the transaction affinity data set, the first pair of temporally related transactions including a first transaction and a second transaction that occurred later in time than the first transaction;
• analyze transaction affinity data associated with each transaction in the first pair of temporally related transactions to determine time gap data associated with the first transaction and the second transaction;
• generate trip transaction data based on the time gap data;
• generate transaction affinity relationship data based on the trip transaction data;
• generate, based on the transaction affinity relationship data, merchant recommendation data associated with a set of recommended merchants that have an affinity relationship with a target merchant, such that the merchant recommendation data is generated before a new transaction occurs after the second transaction and the merchant recommendation data identifies a first recommended merchant included in the set of recommended merchants;
• determine merchant parameter information for each merchant in the set of recommended merchants;
• receive input parameter information associated with a set of parameters, the set of parameters associated with parameters included in the merchant parameter information;
• determine a parameter weight value for each parameter in the set of parameters based on the input parameter information;
• analyze the parameter weight values and the merchant parameter information;
• determine an adjustment to the merchant recommendation data based on analyzing the parameter weight values and the merchant parameter information;
• and
• generate, based on the determined adjustment, adjusted merchant recommendation data that identifies a second recommended merchant in the set of recommended merchants in place of the first recommended merchant.