Computer method and system for ranking users in a network community of users

• US 9,367,823 B1
• Filed: 11/07/2008
• Issued: 06/14/2016
• Est. Priority Date: 11/09/2007
• Status: Active Grant

• Current Assignee: Rakuten Group, Inc.
• Original Assignee: Skyword Incorporated
• Inventors: Feder, Helen, Mihalik, John L., Gerace, Thomas A., Sandborg, David A., Meyer, Richard A.
• Primary Examiner(s): Padot, Timothy
• Application Number:
  US12/266,893
• Time in Patent Office: 2,776 Days
• Field of Search: 705/7.11, 705/14.66, 705/319, 707/748
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/063112 Skill-based matching of a person or a group to a task
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 63/101 Access control lists [ACL]
• CPC Class Codes: H04L 63/107 wherein the security policies are location-dependent, e.g. entities privileges depend on current location or allowing specific operations only from locally connected terminals
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

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.

Claims

1. A method for distributing digital content comprising:

in a hardware processor;

storing in a database of an Internet-based social network a plurality of user profiles, different ones of the stored user profiles representing different users in the Internet-based social network and each stored user profile including indications of rank in the Internet-based social network of the respective user represented by the profile;

accessing a search database storing digital content viewed by the users of the Internet-based social network, the digital content being from a plurality of global computer network sites;

identifying an importance of an individual user as a plurality of functions of user demand of the individual user;

determining at least one relative rank of the individual user based on the identified importance, said determining including;

(i) representing the social network as an interconnected network graph having nodes and directional connection lines between the nodes, each node being a respective person and each directional connection line being a connection from a person requesting the connection to a person being connected to, and (ii) modeling the connections between people represented in the interconnected network graph as a mathematical edge-weighted directed graph, wherein the mathematical edge-weighted directed graph mathematically takes into account the entire interconnected network graph at once and derives the importance of the individual user as the plurality of functions of user demand recursively for the entire interconnected network graph, such that the at least one relative rank of the individual user 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 nodes of the mathematical edge-weighted directed graph and the second matrix being based on and representing a number of nodes in the graph so as to account for the entire interconnected network graph;

updating in the respective user profile, the indications of the at least one determined relative rank of the individual user, said user profile stored in the database of user profiles of the social network;

prioritizing at least some of the digital content in the search database as a function of at least the determined relative rank stored in the user profiles of the respective users that viewed the digital content and

distributing the content based on the prioritization,

wherein said hardware processor is configured to perform said storing, identifying, determining, updating, prioritizing, and distributing.

2. A method as claimed in claim 1 wherein the plurality of functions of user demand includes any one or more of:

number of requests to be connected to the individual user;

number of actual connections made to the individual user;

number of messages sent to the individual user;

number of messages sent by the individual user and opened by other users;

number of public comments on a profile of the individual user'"'"'s;

number of requests to view content authored by the individual user;

number of comments on the individual user'"'"'s content;

number of invitations sent to the individual user for any one or more of;

i) membership to the community, ii) membership to groups, and ii) events;

number of chat requests made to the individual user;

number of chat sessions engaged in by the individual user;

number of videoconference requests made to the individual user;

number of videoconference sessions engaged in by the individual user;

number of calls attempted to the individual user;

number of calls received by the individual user;

community members following activities of the individual 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 individual user.

3. A method as claimed in claim 2 wherein the community members following may be topic specific, resulting in different ranks for each user for different topical areas.

4. A method as claimed in claim 1 wherein plurality of functions of user demand of the individual user is normalized according to a total number of requests to be connected to any of the global computer network users per global computer network user.

5. A method as claimed in claim 1 wherein determining the at least one rank includes recursively using ranks of people connecting to the individual user, wherein each person has a respective rank based on his identified importance, his identified importance being a function of demand for him.

6. A method as claimed in claim 1 further comprising, in the database, decreasing the at least one rank of the individual user if the individual user engages in behavior that is negative within the community.

7. A method as claimed in claim 6 further comprising establishing a watch list of individuals whose activities may be limited by system administrators.

8. A method as claimed in claim 1 further comprising decreasing the at least one rank of the individual user when connection requests sent by the individual user to other people are declined or ignored.

9. A method as claimed in claim 1 further comprising sorting results of searching for members of the community, where the sorting includes the at least one ranks of the members as a factor.

10. A method as claimed in claim 1 further comprising sorting search results for content, where the sorting includes the at least one ranks of authors of the content as a factor.

11. A method as claimed in claim 1 further comprising:

for each content, collecting information of users that browse the content including collecting rankings of said users; and

organizing search results for content using a function of ranks of users browsing the content.

12. A method as claimed in claim 1 further comprising:

collecting user recommendations of at least one of content and a global computer network page; and

organizing the recommendations for at least one of search results and navigation using a function of ranks of users that made the recommendations.

13. A method as claimed in claim 1 wherein members may opt not to see an indication of a user below a certain rank.

14. A method as claimed in claim 1 further comprising providing the at least one rank of the individual user as an input into fraud detection algorithms.

15. A method as claimed in claim 1 wherein the at least one rank is visible as one or more of i) a number;

and ii) a tier level demonstrating status.

16. A method as claimed in claim 1 wherein the at least one rank is used in compensating the individual user for participation or publishing in the community.

17. A method as claimed in claim 1 wherein members have different privileges or technical functionality based on their rank.

18. A method as claimed in claim 1 further comprising assigning a new member to the community, who is invited to the community by another member, an initial rank upon sign-up based on the at least one rank of the other member who invited the new member.

19. A method as claimed in claim 18 in which the initial rank is assigned differently to new members to the community who register without being invited than to new members who are invited to join.

20. A method as claimed in claim 1 further comprising providing the at least one rank of the individual user to an entity external from the community.

21. A method as claimed in claim 20 further comprising providing the at least one rank to an advertising targeting engine for the purpose of targeting advertising to people using one or more of a website and an identifiable telecommunications device for browsing.

22. A method as claimed in claim 1 wherein user rank in any one or more of areas of expertise, areas of interest, and profile characteristics is used to identify a group of people to invite to do any one or more of the following:

try a product;

receive an invitation;

receive a sample;

receive a preview; and

receive preferred status for a product.

23. A method as claimed in claim 21 further comprising determining advertisements that will be displayed to a user based on any one or more of user rank, profile characteristics, areas of interest, and geography.

24. A method as claimed in claim 21 further comprising pricing advertisements based on the rank distribution of people who will receive the advertisements.

25. A method as claimed in claim 1 further comprising identifying experts to do any one or more of creating content, commenting on proposals, participating in discussion, and reviewing publications;

wherein the identifying is based on any one or more of user rank, profile characteristics, and areas of interest.

26. A method as claimed in claim 25 wherein the identifying is based on geography to identify experts in a specific location.

27. A computer system for distributing digital content comprising:

a database of an Internet-based social network containing a plurality of user profiles, different ones of the stored user profiles representing different users in the Internet-based social network, each stored user profile including indications of rank in the Internet-based social network of the respective user represented by the profile;

a search database containing digital content viewed by the users of the Internet-based social network, the digital content being from a plurality of global computer network sites;

a processor configured to identify an importance of the individual user as a plurality of functions of user demand;

the processor further configured to execute a computer program comprising instructions for execution by a processor, from a non-transitory computer readable storage medium, the computer program adapted for;

determining at least one relative rank of the individual user based on the identified importance, said determining including;

(i) representing the social network as an interconnected network graph having nodes and directional connection lines between the nodes, each node being a respective person and each directional connection line being a connection from a person requesting the connection to a person being connected to, and (ii) modeling the connections between people represented in the interconnected network graph as a mathematical edge-weighted directed graph, wherein the mathematical edge-weighted directed graph mathematically takes into account the entire interconnected network graph at once and derives the importance of the individual user as the plurality of functions of user demand recursively for the entire interconnected network graph, such that the at least one relative rank of the individual user 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 nodes of the mathematical edge-weighted directed graph and the second matrix being based on and representing a number of nodes in the graph so as to account for the entire interconnected network graph; and

updating in the user profile, the indications of the at least one determined relative rank of the individual user, said user profile corresponding to the individual user and being stored in the database of user profiles of the social network;

prioritizing at least some of the digital content in the search database as a function of at least the determined relative rank stored in the user profiles of the respective users that viewed the digital content and

distributing the digital content at least based on the prioritization.

28. A computer system as claimed in claim 27 wherein the plurality of functions of user demand includes any one or more of:

number of requests to be connected to the individual user;

number of actual connections made to the individual user;

number of messages sent to the individual user;

number of messages sent by the individual user and opened by other users;

number of public comments on a profile of the individual user'"'"'s;

number of requests to view content authored by the individual user;

number of comments on the individual user'"'"'s content;

number of invitations sent to the individual user for any one or more of;

i) membership to the community, ii) membership to groups, and ii) events;

number of chat requests made to the individual user;

number of chat sessions engaged in by the individual user;

number of videoconference requests made to the individual user;

number of videoconference sessions engaged in by the individual user;

number of calls attempted to the individual user;

number of calls received by the individual user;

community members following activities of the individual 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 individual user.

29. A computer system as claimed in claim 28 wherein the community members following may be topic specific, resulting in different ranks for each user for different topical areas.

30. A computer system as claimed in claim 27 wherein the plurality of functions of user demand is normalized according to a total number of requests to be connected to any of the global computer network users per global computer network user.

31. A computer system as claimed in claim 27 wherein determining the at least one rank includes recursively using ranks of people connecting to the individual user, wherein each person has a respective rank based on his identified importance, his identified importance being a function of demand for him.

32. A computer system as claimed in claim 27 wherein the processor is further configured to decrease, in the database, the at least one rank of the individual user if the individual user engages in behavior that is negative within the community.

33. A computer system as claimed in claim 32 further comprising a watch list of individuals whose activities may be limited by system administrators.

34. A computer system as claimed in claim 27 wherein the processor is further configured to decrease the at least one rank of the individual user when connection requests sent by the individual user to other people are declined or ignored.

35. A computer system as claimed in claim 27 wherein the processor is further configured to sort results of searching for members of the community, where the sorting includes the ranks of the members as a factor.

36. A computer system as claimed in claim 27 wherein the processor is further configured to sort search results for content, where the sorting includes the ranks of authors of the content as a factor.

37. A computer system as claimed in claim 27 wherein the processor is further configured to:

collect information of users who are browsing content, including collecting ranks of said users; and

organize search results for content using a function of ranks of users browsing the content.

38. A computer system as claimed in claim 27 wherein members may opt not to see an indication of a user below a certain rank.

39. A computer system as claimed in claim 27 wherein the processor is further configured to provide the at least one rank of the individual user as an input into fraud detection system.

40. A computer system as claimed in claim 27 wherein the at least one rank is visible as one or more of i) a number;

and ii) a tier level demonstrating status.

41. A computer system as claimed in claim 27 wherein the at least one rank is used in compensating the individual user for participation or publishing in the community.

42. A computer system as claimed in claim 27 wherein members have different privileges or technical functionality based on their rank.

43. A computer system as claimed in claim 27 wherein the processor is further configured to assign a new member to the community, who is invited to the community by another member, an initial rank upon sign-up based on the at least one rank of the other member who invited the new member.

44. A computer system as claimed in claim 43 in which the initial rank is assigned differently to new members to the community who register without being invited than to new members who are invited to join.

45. A computer system as claimed in claim 27 wherein the processor is further configured to provide the at least one rank of the individual user to an entity external from the community.

46. A computer system as claimed in claim 27 wherein user rank in any one or more of areas of expertise, areas of interest, and profile characteristics is used to identify a group of people to invite to do any one or more of the following:

try a product;

receive an invitation;

receive a sample;

receive a preview; and

receive preferred status for a product.

47. A computer system as claimed in claim 27 wherein the processor is further configured to identify experts to do any one or more of creating content, commenting on proposals, participating in discussion, and reviewing publications;

wherein the identifying is based on any one or more of user rank, profile characteristics, and areas of interest.

48. A computer system as claimed in claim 47 wherein the identifying is based on geography to identify experts in a specific location.