Reputation Management in a Transaction Processing System

Abstract

A method includes: receiving information regarding a plurality of completed transactions from a plurality of users; receiving a query from a first user regarding a proposed transaction; determining at least one affinity between the first user and the plurality of users based on the information; determining a ranking or expectation of success for each of a plurality of potential entities for the proposed transaction based on the at least one affinity; selecting a plurality of selected entities based on the ranking or expectation of success for each of the potential entities; and sending, in response to the query, the plurality of selected entities to the first user.

What is claimed is: 
     
         1 . A computer-implemented method, comprising:
 receiving, with at least one processor of a reputation system, transaction data for a plurality of transactions for a plurality of entities including a first user;   clustering, with the at least one processor, the plurality of entities into a plurality of cluster groups;   determining, with the at least one processor, affinities between clusters based on the transaction data;   receiving, with the at least one processor from the first user, a query associated with a transaction;   determining, with the at least one processor, a plurality of reputation scores or ranks for the transaction based on at least one affinity between a first cluster group including the first user and at least one other cluster group including other entities of the plurality of entities;   transmitting, with the at least one processor to the first user, a set of entities of the plurality of entities based on the plurality of reputation scores or ranks;   selecting, with the at least one processor, a set of entities from the plurality of entities based on the at least one score or rank; and   transmitting, with the at least one processor to the first user, the set of entities.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising:
 receiving feedback from the first user or at least one other entity regarding the transaction; and   updating an affinity between the first cluster group and the at least one other cluster group based on the feedback.   
     
     
         3 . The computer-implemented method of  claim 1 , wherein the transaction data comprises data associated with transactions to which a transactional token is applied by an entity of the plurality of entities. 
     
     
         4 . The computer-implemented method of  claim 3 , further comprising storing a token data record for the transactional token in a memory or database of a token processing system, wherein the determining the ranking or expectation of success comprises using information from the token data record. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the affinities are determined based on social network data. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the reputation system comprises a transaction handler configured to process transactions between the plurality of entities. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the plurality of reputation scores or ranks are relative to the first cluster group and the at least one other cluster group. 
     
     
         8 . The computer-implemented method of  claim 1 , wherein the plurality of reputation scores or ranks are relative to the transaction. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein transmitting the set of entities to the first user comprises transmitting an emoticon representing a reputation score or rank for each entity of the set of entities. 
     
     
         10 . A reputation system comprising:
 a data warehouse comprising transaction data for a plurality of transactions for a plurality of entities including a first user;   at least one processor in communication with the data warehouse, the at least one processor programmed or configured to:   receive transaction data for a plurality of transactions for a plurality of entities including a first user;   cluster the plurality of entities into a plurality of cluster groups;   determine affinities between clusters based on the transaction data;   receive, from the first user, a query associated with a transaction;   determine a plurality of reputation scores or ranks for the transaction based on at least one affinity between a first cluster group including the first user and at least one other cluster group including other entities of the plurality of entities;   transmit, to the first user, a set of entities of the plurality of entities based on the plurality of reputation scores or ranks;   select a set of entities from the plurality of entities based on the at least one score or rank; and   transmit the set of entities to the first user.   
     
     
         11 . The reputation system of  claim 10 , wherein the at least one processor is further configured to:
 receive feedback from the first user or at least one other entity regarding the transaction; and   update an affinity between the first cluster group and the at least one other cluster group based on the feedback.   
     
     
         12 . The reputation system of  claim 10 , wherein the transaction data comprises data associated with transactions to which a transactional token is applied by an entity of the plurality of entities. 
     
     
         13 . The reputation system of  claim 12 , wherein the at least one processor is further configured to store a token data record for the transactional token in a memory or database of a token processing system, wherein the determining the ranking or expectation of success comprises using information from the token data record. 
     
     
         14 . The reputation system of  claim 10 , wherein the affinities are determined based on social network data. 
     
     
         15 . The reputation system of  claim 10 , further comprising a transaction handler configured to process transactions between the plurality of entities, the transaction handler comprising the at least one processor. 
     
     
         16 . The reputation system of  claim 10 , wherein the plurality of reputation scores or ranks are relative to the first cluster group and the at least one other cluster group. 
     
     
         17 . The reputation system of  claim 10 , wherein the plurality of reputation scores or ranks are relative to the transaction. 
     
     
         18 . The reputation system of  claim 10 , wherein transmitting the set of entities to the first user comprises transmitting an emoticon representing a reputation score or rank for each entity of the set of entities. 
     
     
         19 . A computer program product comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to:
 receive transaction data for a plurality of transactions for a plurality of entities including a first user;   cluster the plurality of entities into a plurality of cluster groups;   determine affinities between clusters based on the transaction data;   receive, from the first user, a query associated with a transaction;   determine a plurality of reputation scores or ranks for the transaction based on at least one affinity between a first cluster group including the first user and at least one other cluster group including other entities of the plurality of entities;   transmit, to the first user, a set of entities of the plurality of entities based on the plurality of reputation scores or ranks;   select a set of entities from the plurality of entities based on the at least one score or rank; and   transmit the set of entities to the first user.   
     
     
         20 . The computer program product of  claim 19 , wherein the program instructions further cause the at least one processor to:
 receive feedback from the first user or at least one other entity regarding the transaction; and   update an affinity between the first cluster group and the at least one other cluster group based on the feedback.