Systems and methods of location based merchant recommendations

• US 10,163,147 B2
• Filed: 12/11/2015
• Issued: 12/25/2018
• Est. Priority Date: 12/11/2015
• Status: Active Grant

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Lee, Edward M., Zhang, Tong, Wang, Qian
• Primary Examiner(s): Civan, Ethan D
• Application Number:
  US14/966,838
• Publication Number:
  US 20170169497A1
• Time in Patent Office: 1,110 Days
• Field of Search: 705 261- 272
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A method for generating a list of recommended merchants based on an input merchant is provided. The method uses a recommender computing device. The method includes receiving an input merchant identifier, retrieving a first electronic data signal based on the input merchant identifier including historical transaction data of the input merchant including historical payment transactions initiated by candidate cardholders with the input merchant, and storing a list of the candidate cardholders. The method further includes retrieving a second electronic data signal that includes historical transaction data for at least some of the candidate cardholders included in the list of candidate cardholders and a candidate merchant identifier that identifies the candidate merchants, generating a list of candidate merchants from the second data signal including a ranking of the candidate merchants, and generating a list of recommended merchants based on the list of candidate merchants.

Claims

1. A method for generating a list of recommended merchants based, at least in part, on an input merchant, the method implemented using a recommender computing device in communication with a memory and a payment processor, said method comprising:

receiving, by the recommender computing device, an input merchant identifier for identifying the input merchant;

enrolling, by the recommender computing device using the input merchant identifier, the input merchant in a recommendation program;

storing, by the recommender computing device, the input merchant identifier within the memory;

receiving, by the recommender computing device, a request for merchant recommendations from a merchant user device associated with the input merchant;

retrieving, by the recommender computing device, a first electronic data signal from a database associated with the payment processor based at least in part on the input merchant identifier, the first electronic data signal including historical transaction data of the input merchant including a plurality of historical payment transactions having been initiated by candidate cardholders with the input merchant;

storing, by the recommender computing device, a list of the candidate cardholders in the memory from the first electronic data signal;

retrieving, by the recommender computing device, a second electronic data signal from the payment processor database, the second electronic data signal including historical transaction data for at least some of the candidate cardholders included in the list of candidate cardholders, the historical transaction data for the candidate cardholders including one or more candidate merchant identifiers, each of the one or more candidate merchant identifiers identifying a candidate merchant of a plurality of candidate merchants, each candidate merchant of the plurality of candidate merchants involved in at least one transaction of the plurality of historical payment transactions;

generating, by the recommender computing device, a list of candidate merchants from the second electronic data signal including a ranking of the plurality of candidate merchants;

generating by the recommender computing device, a list of recommended merchants based on the list of candidate merchants and the ranking; and

transmitting, by the recommender computing device, the list of recommended merchants to the merchant user device.

2. The method of claim 1, further comprising advancing counters associated for at least a subset of the plurality of candidate merchants identified in the second electronic data signal based on each instance of the one or more candidate merchant identifiers being included in the second electronic data signal.

3. The method of claim 2, wherein the list of recommended merchants includes a subset of the list of candidate merchants in descending order of corresponding counter values.

4. The method of claim 1, further comprising:

retrieving, from the second electronic data signal, category information associated with the plurality of candidate merchants; and

categorizing the plurality of candidate merchants by assigning a category to each candidate merchant based on the category information,

wherein the list of recommended merchants includes the plurality of candidate merchants listed by category and by rank.

5. The method of claim 4, wherein the category information is at least one of a merchant category code, an industry code, or a retail industry code.

6. The method of claim 4, wherein the category is at least one of a restaurant category, an entertainment category, an attraction category, or a shopping category.

7. The method of claim 1, further comprising:

querying a remote database with a plurality of candidate merchant identifiers;

requesting category information for the plurality of candidate merchants associated with the plurality of candidate merchant identifiers; and

receiving the requested category information,

wherein the list of recommended merchants includes the plurality of candidate merchants listed by category based on the category information received from the remote database, and by rank.

8. The method of claim 1, further comprising providing the list of recommended merchants to at least one of an input merchant customer, a merchant bank, an application running on a user device, or a hotel booking service.

9. The method of claim 1, further comprising transmitting instructions to a user device running an application, wherein the instructions are formatted to cause the application to display the list of recommended merchants.

10. The method of claim 1, further comprising generating historical transaction data for a plurality of input merchants by filtering a plurality of transaction signals to remove all signals which do not correspond to a transaction at a hotel using a merchant category code included in the plurality of transaction signals, and sorting the transactions signals based on input merchant identifiers included in the transaction signals and associated with each of the plurality of input merchants.

11. The method of claim 1, further comprising:

comparing, for each candidate cardholder, an input merchant location, based on location data corresponding to the input merchant and included in the first electronic data signal, with a residence location of the candidate cardholder;

determining, for each candidate cardholder, if the candidate cardholder residence location is greater than a predetermined distance from the input merchant location; and

excluding, for each candidate cardholder, the historical transaction data associated with the candidate cardholder in response to determining that the candidate cardholder residence location is not greater than the predetermined distance from the input merchant location.

12. The method of claim 11, wherein the candidate cardholder residence location is based on either (a) a known zip code of the candidate cardholder residence location or (b) a modeled zip code of the candidate cardholder residence location determined based on the candidate cardholder'"'"'s previous transactions.

13. The method of claim 1, further comprising:

comparing, for each candidate merchant, an input merchant location, based on location data corresponding to the input merchant and included in the first electronic data signal, with a candidate merchant location, based on location data included in the second electronic data signal;

determining, for each candidate merchant, if the candidate merchant location is within a predetermined distance from the input merchant location; and

excluding, for each candidate merchant, the historical transaction data including a corresponding candidate merchant identifier in response to determining that the associated candidate merchant location is not within a predetermined distance from the input merchant location.

14. The method of claim 1, further comprising:

comparing, for each transaction included in the historical transaction data for each cardholder, a first time stamp associated with the transaction with a second time stamp of a transaction having the same candidate cardholder identifier and included in the historical transaction data of the input merchant;

determining, for each transaction included in the historical transaction data for each cardholder, if the first time stamp and the second time stamp are within a predetermined time range; and

excluding transaction data included in the historical transaction data for each cardholder in response to determining that the first time stamp and the second time stamp are not with the predetermined time range.

15. The method of claim 1, further comprising:

determining, for each transaction of the historical transaction data for each candidate cardholder, if the historical transaction data associated with the transaction includes a card present transaction type identifier; and

excluding the historical transaction data associated with the transaction in response to determining that the transaction does not include a card present transaction type identifier.

16. A recommender computing device comprising at least one processor in communication with a memory, said recommender computing device in communication with a payment processor, said at least one processor programmed to:

receive an input merchant identifier for identifying the input merchant;

enroll, using the input merchant identifier, the input merchant in a recommendation program;

store the input merchant identifier within the memory;

receive a request for merchant recommendations from a merchant user device associated with the input merchant;

retrieve a first electronic data signal from a database associated with the payment processor based at least in part on the input merchant identifier, the first electronic data signal including historical transaction data of the input merchant including a plurality of historical payment transactions having been initiated by candidate cardholders with the input merchant;

store a list of the candidate cardholders in the memory from the first electronic data signal;

retrieve a second electronic data signal from the payment processor database, the second electronic data signal including historical transaction data for at least some of the candidate cardholders included in the list of candidate cardholders, the historical transaction data for the candidate cardholders including one or more candidate merchant identifiers, each of the one or more candidate merchant identifiers identifying a candidate merchant of a plurality of candidate merchants, each candidate merchant of the plurality of candidate merchants involved in at least one transaction of the plurality of historical payment transactions;

generate a list of candidate merchants from the second electronic data signal including a ranking of the plurality of candidate merchants;

generate a list of recommended merchants based on the list of candidate merchants and the ranking; and

transmit the list of recommended merchant to the merchant user device.

17. The recommender computing device of claim 16, wherein said at least one processor is further programmed to:

query a remote database with a plurality of candidate merchant identifiers;

request category information for the plurality of candidate merchants associated with the plurality of candidate merchant identifiers; and

receive the requested category information,

wherein the list of recommended merchants includes the plurality of candidate merchants listed by category based on the category information received from the remote database, and by rank.

18. The recommender computing device of claim 16, wherein said at least one processor is further programmed to:

compare, for each candidate cardholder, an input merchant location, based on location data corresponding to the input merchant and included in the first electronic data signal, with a residence location of the candidate cardholder;

determine, for each candidate cardholder, if the candidate cardholder residence location is greater than a predetermined distance from the input merchant location; and

exclude, for each candidate cardholder, the historical transaction data associated with the candidate cardholder in response to determining that the candidate cardholder residence location is not greater than the predetermined distance from the input merchant location.

19. The recommender computing device of claim 16, wherein said at least one processor is further programmed to:

compare, for each transaction included in the historical transaction data for each cardholder, a first time stamp associated with the transaction with a second time stamp of a transaction having the same candidate cardholder identifier and included in the historical transaction data of the input merchant;

determine, for each transaction included in the historical transaction data for each cardholder, if the first time stamp and the second time stamp are within a predetermined time range; and

exclude transaction data included in the historical transaction data for each cardholder in response to determining that the first time stamp and the second time stamp are not with the predetermined time range.

20. A computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by a recommender computing device including at least one processor in communication with a memory, the recommender computing device in communication with a payment processor, the computer-executable instructions cause the recommender computing device to:

receive an input merchant identifier for identifying the input merchant;

enroll, using the input merchant identifier, the input merchant in a recommendation program;

store the input merchant identifier within the memory;

receive a request for merchant recommendations from a merchant user device associated with the input merchant;

retrieve a first electronic data signal from a database associated with the payment processor based at least in part on the input merchant identifier, the first electronic data signal including historical transaction data of the input merchant including a plurality of historical payment transactions having been initiated by candidate cardholders with the input merchant;

store a list of the candidate cardholders in the memory from the first electronic data signal;

retrieve a second electronic data signal from the payment processor database, the second electronic data signal including historical transaction data for at least some of the candidate cardholders included in the list of candidate cardholders, the historical transaction data for the candidate cardholders including one or more candidate merchant identifiers, each of the one or more candidate merchant identifiers identifying a candidate merchant of a plurality of candidate merchants, each candidate merchant of the plurality of candidate merchants involved in at least one transaction of the plurality of historical payment transactions;

generate a list of candidate merchants from the second electronic data signal including a ranking of the plurality of candidate merchants;

generate a list of recommended merchants based on the list of candidate merchants and the ranking; and

transmit the list of recommended merchant to the merchant user device.