Computer method and system for determining expert-users in a computer network
A computer method and system for ranking computer network portal users is provided. The system and method comprise a ranking module which determines rank of an individual in a community of global computer network users. The ranking module determines rank of an individual as a function of user demand. The function of user demand includes any one or combination of number of requests to be connected to the individual user, readership following of the individual user and keywords common between profiles or authored works by the individual user and those of other users. An output member is coupled to receive the determined rank from the ranking module and generates an ordered list of user names ordered by determined rank of individuals. The rank of individuals may be provided to external entities such as fraud detection systems or advertising targeting engines.
1. A computer system for discovering information, the computer system comprising:
at least one computer coupled through a global network to client computers associated with users, the at least one computer having a processor configured to execute a computer program comprising instructions for execution by the processor, from a non-transitory computer readable storage medium, the computer program adapted to:
determine one or more expert-users in a computer-based network of users based on a respective demand for each of the one or more expert-users, each respective demand being based on at least one relative rank of the respective expert-user among the computer-based network of users, the at least one relative rank being derived recursively using an interconnected network graph of connections between users of the computer-based network of users, such that the at least one relative rank is determined after one pass of iterations by determining a product of a first matrix and a second matrix, the first matrix being based on and representing connections between users of the computer-based network in the interconnected network graph and the second matrix being based on and representing a number of users in the interconnected network graph so as to account for the interconnected network graph as a whole,
track computer activity of one of the one or more expert-users on at least one global computer network site,
identify electronic information based on the tracked computer activity of the one expert-user,
store, in a database, the electronic information identified based on the tracked computer activity of the one expert-user,
prioritize at least some of the identified stored electronic information as a function of the at least one relative rank of the one expert-user, and
distribute at least some of the identified stored electronic information based on the prioritization.
2. The computer system of claim 1 , wherein the computer program is further adapted to distribute the identified stored information using a recommendation engine configured to recommend the tracked computer activity of the one expert-user.
3. The computer system of claim 1 , wherein the one or more expert-users are determined based on a respective demand for each of the one or more expert-users in a category of content, wherein each respective demand is based on at least one relative rank of the respective expert-user among the computer-based network of users in the category of content.
4. The computer system of claim 1 , wherein the computer program is further adapted to distribute the identified stored electronic information by organizing search results as a function of the prioritization.
5. The computer system of claim 1 , wherein the respective demand for each of the one or more expert-users includes any one or more of:
number of requests to be connected to the expert-user;
number of actual connections made to the expert-user;
number of messages sent to the expert-user;
number of messages sent by the expert-user and opened by other users;
number of public comments on a profile of the expert-user;
number of requests to view content authored by the expert-user;
number of comments on the expert-user's content;
number of invitations sent to the expert-user for any one or more of: i) membership to a community, ii) membership to groups, and ii) events;
number of chat requests made to the expert-user;
number of chat sessions engaged in by the expert-user;
number of videoconference requests made to the expert-user;
number of videoconference sessions engaged in by the expert-user;
number of calls attempted to the expert-user;
number of calls received by the expert-user;
community members following activities of the expert-user activity within the network; and
keywords common between any one or combination of a profile and content created by a user and any one or combination of a profile and content of the expert-user.
6. A computer-implemented method for discovering information, the computer-implemented method comprising:
in a hardware processor:
determining one or more expert-users in a computer-based network of users based on a respective demand for each of the one or more expert-users, each respective demand being based on at least one relative rank of the respective expert-user among the computer-based network of users, the at least one relative rank being derived recursively using an interconnected network graph of connections between users of the computer-based network of users, such that the at least one relative rank is determined after one pass of iterations by determining a product of a first matrix and a second matrix, the first matrix being based on and representing connections between users of the computer-based network in the interconnected network graph and the second matrix being based on and representing a number of users in the interconnected network graph so as to account for the interconnected network graph as a whole,
tracking computer activity of one of the one or more expert-users on at least one global computer network site,
identifying electronic information based on the tracked computer activity of the one expert-user,
storing, in a database, the electronic information identified based on the tracked computer activity of the one expert-user,
prioritizing at least some of the identified stored electronic information as a function of the at least one relative rank of the one expert-user, and
distributing at least some of the identified stored information based on the prioritization,
wherein said hardware processor is configured to perform said determining, tracking, identifying, storing, prioritizing and distributing.
7. The computer-implemented method of claim 6 , wherein distributing the identified stored electronic information includes recommending the tracked computer activity of the one expert-user.
8. The computer-implemented method of claim 6 , wherein the one or more expert-users are determined based on a respective demand for each of the one or more expert-users in a category of content, wherein each respective demand is based on at least one relative rank of the respective expert-user among the computer-based network of users in the category of content.
9. The computer-implemented method of claim 6 , wherein distributing the identified stored electronic information includes organizing search results as a function of the prioritization.
10. The computer-implemented method of claim 6 , wherein the respective demand for each of the one or more expert-users includes any one or more of:
number of requests to be connected to the expert-user;
number of actual connections made to the expert-user;
number of messages sent to the expert-user;
number of messages sent by the expert-user and opened by other users;
number of public comments on a profile of the expert-user;
number of requests to view content authored by the expert-user;
number of comments on the expert-user's content;
number of invitations sent to the expert-user for any one or more of: i) membership to a community, ii) membership to groups, and ii) events;
number of chat requests made to the expert-user;
number of chat sessions engaged in by the expert-user;
number of videoconference requests made to the expert-user;
number of videoconference sessions engaged in by the expert-user;
number of calls attempted to the expert-user;
number of calls received by the expert-user;
community members following activities of the expert-user activity within the network; and
keywords common between any one or combination of a profile and content created by a user and any one or combination of a profile and content of the expert-user.