Ranking merchants

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.

What is claimed is: 
     
         1 . A method comprising:
 determining, by a computer-based system, a consumer preference associated with a type of merchant based on transaction data associated with a consumer,   determining, by the computer-based system, a consumer relevance value associated with a merchant based upon the consumer preference;   generating, by the computer-based system, a merchant matrix having coefficients indicating that the merchants are associated,   adjusting, by the computer-based system, the consumer relevance value based on the association between merchants from the merchant matrix and the coefficients,   comparing, by the computer-based system and based on matching rules, criteria associated with the merchant with social data from the social media website about the merchant to determine a social media association between the criteria for the merchant and the social media data about the merchant;   providing, by the computer-based system, a higher ranking for the merchant based on the social media association and the consumer relevance value;   adjusting, by the computer-based system, the consumer relevance value of the merchant based on real time transaction information; and   ranking, by the computer-based system, the plurality of merchants in a ranked list based on the real time transaction information.   
     
     
         2 . The method of  claim 1 , further comprising:
 storing, by the computer-based system, data sets of the consumer relevance value in a database as ungrouped data elements formatted as a block of binary (BLOB) via a fixed memory offset;   partitioning, by the computer-based system and using a key field, the database according to a class of objects defined by the key field to speed searching for the consumer relevance value;   linking, by the computer-based system, data tables based on the type of data in the key fields;   annotating, by the computer-based system, the data sets to include security information establishing access levels; and   obtaining, by the computer-based system, the consumer relevance value from the database.   
     
     
         3 . The method of  claim 1 , further comprising adjusting, by the computer-based system, the consumer relevance value associated with the merchant based upon at least one of an industry of the merchant, a consumer profile, a transaction history associated with the consumer, social data, demographic data, clickstream data or consumer feedback data. 
     
     
         4 . The method of  claim 1 , further comprising adjusting, by the computer-based system, the consumer relevance value associated with the merchant based upon a plurality of merchant to merchant similarity values. 
     
     
         5 . The method of  claim 4 , wherein the merchant to merchant similarity value is based on pairings of merchants at least one of occurring most often or are most strongly correlated. 
     
     
         6 . The method of  claim 4 , wherein the merchant to merchant similarity value is determined by comparing record of charges (ROCs) of a plurality of consumers at the plurality of merchants. 
     
     
         7 . The method of  claim 4 , wherein the merchant to merchant similarity between a first merchant and the plurality of merchants is a result of ROCs by a first consumer at a first merchant and ROCs by a second consumer at the plurality of merchants including the first merchant. 
     
     
         8 . The method of  claim 4 , wherein a first merchant to merchant similarity between a third merchant and the first merchant and a second merchant to merchant similarity between the third merchant and a second merchant is not as strong of a similarity as between the first merchant and the second merchant from whom each consumer conducted a transaction. 
     
     
         9 . The method of  claim 1 , wherein each of the coefficients includes a record of charge (ROC) of the consumer with the merchant, wherein the ROC indicates the consumer conducted a transaction with the merchant, wherein the ROC includes a number of transactions the consumer conducted with the merchant. 
     
     
         10 . The method of  claim 1 , further comprising further adjusting, by the computer-based system, the consumer relevance value based on at least one of a collaborative filtering, the merchant goal and a business rule. 
     
     
         11 . The method of  claim 10 , wherein the merchant goal is at least one of acquiring only new consumers, tailoring existing consumers of the merchant, or tailoring all consumers. 
     
     
         12 . The method of  claim 10 , wherein the business rule is at least one of a holiday, a particular time of day, the consumer is traveling, the merchant is associated with another merchant who is a particular distance away or near to a location of the consumer, the consumer has indicated a preference not to receive the offers associated with the merchant. 
     
     
         13 . The method of  claim 1 , wherein the consumer preference is further based on at least one of industry codes associated with the transaction data, transactions with merchants in similar industry groups, groups of similar merchants, mapping third party data to internal data, a consumer profile, social data, time information, date information, or feedback. 
     
     
         14 . The method of  claim 1 , wherein the consumer preference is based on a categorization associated with the merchant. 
     
     
         15 . The method of  claim 1 , further comprising determining, by the computer-based system and by using at least one of a co-occurrence method or a cosine method, the association between merchants in the merchant matrix. 
     
     
         16 . The method of  claim 1 , further comprising up-ranking, by the computer-based system, the merchant in a ranked list of merchants based upon the consumer preference. 
     
     
         17 . The method of  claim 1 , wherein the merchants are associated based on at least one of a consumer with the merchant, the merchant with an interest, a consumer with a demographic, the merchant with a demographic, a consumer with another consumer, a consumer's profile with another consumer's profile, a share of wallet with a consumer, a size of wallet with a consumer, an age attribute with a consumer, a gender with a consumer, or a favorite cuisine with a consumer. 
     
     
         18 . The method of  claim 1 , further comprising recommending, by the computer-based system, the merchant based upon the consumer relevance value. 
     
     
         19 . An article of manufacture including a non-transitory, tangible computer readable storage medium having instructions stored thereon that, in response to execution by a computer-based system, cause the computer-based system to be capable of performing operations comprising:
 determining, by the computer-based system, a consumer preference associated with a type of merchant based on transaction data associated with a consumer,   determining, by the computer-based system, a consumer relevance value associated with a merchant based upon the consumer preference;   generating, by the computer-based system, a merchant matrix having coefficients indicating that the merchants are associated,   adjusting, by the computer-based system, the consumer relevance value based on the association between merchants from the merchant matrix and the coefficients,   comparing, by the computer-based system and based on matching rules, criteria associated with the merchant with social data from the social media website about the merchant to determine a social media association between the criteria for the merchant and the social media data about the merchant;   providing, by the computer-based system, a higher ranking for the merchant based on the social media association and the consumer relevance value;   adjusting, by the computer-based system, the consumer relevance value of the merchant based on real time transaction information; and   ranking, by the computer-based system, the plurality of merchants in a ranked list based on the real time transaction information.   
     
     
         20 . A system comprising:
 a processor,   a tangible, non-transitory memory configured to communicate with the processor,   the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to be capable of performing operations comprising:   determining, by the processor, a consumer preference associated with a type of merchant based on transaction data associated with a consumer,   determining, by the processor, a consumer relevance value associated with a merchant based upon the consumer preference;   generating, by the processor, a merchant matrix having coefficients indicating that the merchants are associated,   adjusting, by the processor, the consumer relevance value based on the association between merchants from the merchant matrix and the coefficients,   comparing, by the processor and based on matching rules, criteria associated with the merchant with social data from the social media website about the merchant to determine a social media association between the criteria for the merchant and the social media data about the merchant;   providing, by the processor, a higher ranking for the merchant based on the social media association and the consumer relevance value;   adjusting, by the processor, the consumer relevance value of the merchant based on real time transaction information; and   ranking, by the processor, the plurality of merchants in a ranked list based on the real time transaction information.