Merchant suggestions based on a merchant score

1. A method comprising:

generating, by a computing device, a merchant score for each of a plurality of merchants based at least in part on merchant statistics associated with a plurality of customer visitation trends for the plurality of merchants;

causing, by the computing device, display of a user interface on a user device of a user, wherein the user interface includes a subset of the merchants that have been identified for the user based at least in part on the merchant score;

receiving, by the computing device, clickstream data from the user interface displayed on the user device, wherein the clickstream data comprises a user preference and a user-specified sort of the subset of the merchants on the user interface;

generating, by the computing device, an updated merchant score based at least in part on the user preference;

determining, by the computing device, a sorting habit for the user based at least in part on the user-specified sort of the subset of the merchants on the user interface; and

causing, by the computing device, display on the user interface a modified subset of the merchants that is different from the subset of the merchants, wherein the modified subset of the merchants is sorted for display based at least in part on the updated merchant score and the sorting habit.

2. The method of claim 1 , wherein the merchant score is generated based at least in part on at least one of a plurality of spend predictions of the user, a plurality of merchant profiles, a spend frequency of the user, or historical demographic data of a plurality of users.

3. The method of claim 1 , further comprising: creating, by the computing device, a plurality of merchant-to-merchant similarity matrices based at least in part on transaction data of the user, wherein the merchant score is based at least in part on the plurality of merchant-to-merchant similarity matrices.

4. The method of claim 1 , wherein the user preference is based at least in part on receiving a selection of a characteristic associated with the subset of the merchants from the user interface.

5. The method of claim 1 , wherein the merchant score is generated based at least in part on at least one of a plurality of records of charge, historical account information of the user, third party data, social media data, government data, settings of the user, application settings of the user, or interaction data of the user.

6. The method of claim 1 , wherein the clickstream data further comprises a viewing habit that includes a quantity of search results that are viewed on the user interface.

7. The method of claim 1 , wherein the user comprises a first user, and further comprising generating, by the computing device, second user characteristics for a second user based at least in part on similarity matrices with the first user by using transaction data of the first user and collaborative filtering.

8. A computer-based system, comprising:

a computing device that includes a processor and a memory; and

program instructions stored in the memory that, when executed by the processor, cause the computing device to at least:

generate a merchant score for each of a plurality of merchants based at least in part on merchant statistics associated with a plurality of customer visitation trends for the plurality of merchants;

cause a display of a user interface on a user device of a user, wherein the user interface includes a subset of the merchants that have been identified for the user based at least in part on the merchant score;

receive clickstream data from the user interface displayed on the user device, wherein the clickstream data comprises a user preference and the user-specified sort of the subset of the merchants on the user interface;

generate an updated merchant score based at least in part on the user preference;

determine a sorting habit for the user based at least in part on the user-specified sort of the subset of the merchants on the user interface; and

cause a display on the user interface a modified subset of the merchants that is different from the subset of the merchants, wherein the modified subset of the merchants is sorted for display based at least in part on the updated merchant score and the sorting habit.

9. The system of claim 8 , wherein the merchant score is generated based at least in part on at least one of a plurality of spend predictions of the user, a plurality of merchant profiles, a spend frequency of the user, or historical demographic data of a plurality of users.

10. The system of claim 8 , wherein the program instructions, when executed by the processor, cause the computing device to at least create a plurality of merchant-to-merchant similarity matrices based at least in part on transaction data of the user.

11. The system of claim 8 , wherein the user preference is based at least in part on receiving a selection of a characteristic associated with the subset of the merchants from the user interface.

12. The system of claim 8 , wherein the merchant score is generated based at least in part on at least one of a plurality of records of charge, historical account information of the user, third party data, social media data, government data, settings of the user, application settings of the user, or interaction data of the user.

13. The system of claim 8 , wherein the clickstream data further comprises a viewing habit that includes a quantity of search results that are viewed on the user interface.

14. The system of claim 8 , wherein the user comprises a first user, and the program instructions, when executed by the processor, cause the computing device to at least generate second user characteristics for a second user based at least in part on similarity matrices with the first user by using transaction data of the first user and collaborative filtering.

15. The system of claim 8 , wherein the program instructions, when executed by the processor, cause the computing device to at least:

generate a plurality of spend predictions for the user based at least in part on transaction data for the user;

generate a first profile for the user based at least in part on user data for the user; and

map the first profile with a plurality of merchant profiles to identify the subset of the merchants for the user based at least in part on the plurality of spend predictions of the user.

16. The system of claim 8 , wherein the program instructions, when executed by the processor, cause the computing device to at least:

receive a search parameter that identifies a travel preference associated with a first profile for the user;

retrieve a plurality of search results matching the travel preference associated with the first profile;

sort the plurality of search results to create a sorted search results list based at least in part on search result characteristics of importance to the user according to the first profile; and

display the sorted search results list on the user interface of the user device.

17. A non-transitory computer readable storage medium having instructions stored thereon that, in response to execution by a processor of a computing device, cause the computing device to at least:

generate a merchant score for each of a plurality of merchants based at least in part on merchant statistics associated with a plurality of customer visitation trends for the plurality of merchants;

causing a display of a user interface on a user device of a user, wherein the user interface includes a subset of the merchants that have been identified for the user based at least in part on the merchant score;

receive clickstream data from the user interface displayed on the user device, wherein the clickstream data comprises a user preference and a user-specified sort of the subset of the merchants on the user interface;

generate an updated merchant score based at least in part on the user preference;

determine a sorting habit for the user based at least in part on the user-specified sort of the subset of the merchants on the user interface; and

cause a display on the user interface a modified subset of the merchants that is different from the subset of the merchants, wherein the modified subset of the merchants is sorted for display based at least in part on the updated merchant score and the sorting habit.

18. The non-transitory computer readable storage medium of claim 17 , wherein the instructions, in response to execution by the processor, cause the computing device to at least:

receive a search parameter that identifies a travel preference associated with a user profile for the user;

retrieve a plurality of search results matching the travel preference associated with the user profile;

sort the plurality of search results to create a sorted search result list based at least in part on search result characteristics of importance to the user according to the user profile; and

display the sorted search results list on the user device.

19. The non-transitory computer readable storage medium of claim 17 , wherein the instructions, in response to execution by the processor, cause the computing device to at least:

generate a plurality of spend predictions for the user based at least in part on transaction data for the user;

generate a user profile for the user based at least in part on user data for the user; and

map the user profile with a plurality of merchant profiles to identify the subset of the merchants for the user based at least in part on the plurality of spend predictions of the user.

20. The non-transitory computer readable storage medium of claim 19 , wherein the instructions, in response to execution by the processor, cause the computing device to at least: search for the subset of the merchants based at least in part on the subset of the merchants appearing in a second transaction history of a second profile of a second user, wherein the second profile at least partially matches the user profile.