Ranking objects by social relevance

• US 8,977,611 B2
• Filed: 10/18/2011
• Issued: 03/10/2015
• Est. Priority Date: 10/18/2011
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Tseng, Erick
• Primary Examiner(s): Lee, Wilson
• Application Number:
  US13/276,216
• Publication Number:
  US 20130097180A1
• Time in Patent Office: 1,239 Days
• Field of Search: None
• US Class Current: 707/728
• CPC Class Codes: G06F 16/38 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]

Abstract

In one embodiment, a system includes one or more computing systems that implement a social networking environment and are operable to access stored information including a plurality of nodes including a first set of user nodes that each correspond to a respective user and a second set of concept nodes that each correspond to a respective concept. The system may generate a match coefficient for the user and concept, representing the degree of relevance of a particular concept node to a particular user node.

Claims

1. A method comprising:

by a computer server, accessing, in a social graph associated with one or more computing systems of a social network environment, a user node representing a particular user, the user node connected to a plurality of attribute nodes; and

by the computer server, for each of a plurality of object nodes in the social graph, each object node connected to a plurality of attribute nodes, calculating a match coefficient between the user node and an individual object node, wherein the calculating comprises;

calculating, for each attribute node in a set of all attribute nodes connected to both the user node and the object node, a first coefficient between the user node and the attribute node and a second coefficient between the object node and the attribute node; and

obtaining the sum-product of all the first and second coefficients.

2. The method of claim 1, wherein the first coefficient decreases as the degree of separation between the user node and the attribute node increases, and the second coefficient decreases as the degree of separation between the object node and the attribute node increases.

3. The method of claim 1, wherein each of the second coefficients is multiplied by a feedback weight for its associated attribute node and object node pair.

4. The method of claim 3, wherein the feedback weight is calculated via a machine learning algorithm.

5. The method of claim 4, wherein the feedback weight for each attribute node and object node pair is calculated by:

monitoring all user node interaction with the object node;

for each user node interaction with the object node;

determining whether the attribute node is also connected to the user node;

as a result of a positive determination;

incrementing the feedback weight; and

as a result of a negative determination, decrementing the feedback weight.

6. The method of claim 5, wherein the type of user node interaction with the object node determines the amount by which the feedback weights are incremented or decremented.

7. The method of claim 1, wherein attribute nodes connected to the particular user node comprises affinities calculated from the social graph.

8. The method of claim 7, wherein the social graph comprises information from the particular user'"'"'s profile.

9. The method of claim 7, wherein the social graph comprises the user'"'"'s actions in the social network environment.

10. The method of claim 7, wherein the social graph comprises the user'"'"'s actions on Internet-connected nodes external to the social network environment.

11. The method of claim 7, wherein the social graph comprises common keywords in the user'"'"'s messages.

12. The method of claim 5, wherein the attribute nodes comprise metadata tags.

13. The method of claim 12, wherein the metadata tags are received from a node administrator associated with a particular object node.

14. The method of claim 13, wherein the received metadata tags are initially assigned a predetermined feedback weight.

15. The method of claim 1, further comprising promoting nodes with a match coefficient exceeding a predetermined threshold to the particular user.

16. The method of claim 1, further comprising ranking all nodes by descending match coefficient for display to the particular user.

17. The method of claim 1, wherein attribute nodes comprise concept nodes.

18. The method of claim 17, wherein object nodes comprise concept nodes.

19. The method of claim 17, wherein concept nodes are dynamically generated by the social graph.

20. One or more computer-readable non-transitory storage media in one or more computing systems, the media embodying logic that is operable when executed to:

access, in a social graph associated with one or more computing systems of a social network environment, a user node representing a particular user, the user node connected to a plurality of attribute nodes; and

for each of a plurality of object nodes in the social graph, each object node connected to a plurality of attribute nodes, calculate a match coefficient between the user node and an individual object node, wherein the calculating comprises;

calculating, for each attribute node in a set of all attribute nodes connected to both the user node and the object node, a first coefficient between the user node and the attribute node and a second coefficient between the object node and the attribute node; and

obtaining the sum-product of all the first and second coefficients.

21. The media of claim 20, wherein the first coefficient decreases as the degree of separation between the user node and the attribute node increases, and the second coefficient decreases as the degree of separation between the object node and the attribute node increases.

22. The media of claim 20, wherein each of the second coefficients is multiplied by a feedback weight for its associated attribute node and object node pair.

23. The media of claim 22, wherein the feedback weight is calculated via a machine learning algorithm.

24. The media of claim 23, wherein the feedback weight for each attribute node and object node pair is calculated by:

monitoring all user node interaction with the object node;

for each user node interaction with the object node;

determining whether the attribute node is also connected to the user node;

as a result of a positive determination;

incrementing the feedback weight; and

as a result of a negative determination, decrementing the feedback weight.

25. The media of claim 24, wherein the type of user node interaction with the object node determines the amount by which the feedback weights are incremented or decremented.

26. The media of claim 20, wherein attribute nodes connected to the particular user node comprises affinities calculated from the social graph.

27. The media of claim 26, wherein the social graph comprises information from the particular user'"'"'s profile.

28. The media of claim 26, wherein the social graph comprises the user'"'"'s actions in the social network environment.

29. The media of claim 26, wherein the social graph comprises the user'"'"'s actions on Internet-connected nodes external to the social network environment.

30. The media of claim 26, wherein the social graph comprises common keywords in the user'"'"'s messages.

31. The media of claim 24, wherein the attribute nodes comprise metadata tags.

32. The media of claim 31, wherein the metadata tags are received from a node administrator associated with a particular object node.

33. The media of claim 32, wherein the received metadata tags are initially assigned a predetermined feedback weight.

34. The media of claim 20, further comprising promoting nodes with a match coefficient exceeding a predetermined threshold to the particular user.

35. The media of claim 20, further comprising ranking all nodes by descending match coefficient for display to the particular user.

36. The media of claim 20, wherein attribute nodes comprise concept nodes.

37. The media of claim 36, wherein object nodes comprise concept nodes.

38. The media of claim 36, wherein concept nodes are dynamically generated by the social graph.

39. A system comprising:

one or more processors; and

a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to;

access, in a social graph associated with one or more computing systems of a social network environment, a user node representing a particular user, the user node connected to a plurality of attribute nodes; and

for each of a plurality of object nodes in the social graph, each object node connected to a plurality of attribute nodes, calculate a match coefficient between the user node and an individual object node, wherein the calculating comprises;

calculating, for each attribute node in a set of all attribute nodes connected to both the user node and the object node, a first coefficient between the user node and the attribute node and a second coefficient between the object node and the attribute node; and

obtaining the sum-product of all the first and second coefficients.

40. The system of claim 39, wherein the first coefficient decreases as the degree of separation between the user node and the attribute node increases, and the second coefficient decreases as the degree of separation between the object node and the attribute node increases.

41. The system of claim 39, wherein each of the second coefficients is multiplied by a feedback weight for its associated attribute node and object node pair.

42. The system of claim 41, wherein the feedback weight is calculated via a machine learning algorithm.

43. The system of claim 42, wherein the feedback weight for each attribute node and object node pair is calculated by:

monitoring all user node interaction with the object node;

for each user node interaction with the object node;

determining whether the attribute node is also connected to the user node;

as a result of a positive determination;

incrementing the feedback weight; and

as a result of a negative determination, decrementing the feedback weight.

44. The system of claim 43, wherein the type of user node interaction with the object node determines the amount by which the feedback weights are incremented or decremented.

45. The system of claim 39, wherein attribute nodes connected to the particular user node comprises affinities calculated from the social graph.

46. The system of claim 45, wherein the social graph comprises information from the particular user'"'"'s profile.

47. The system of claim 45, wherein the social graph comprises the user'"'"'s actions in the social network environment.

48. The system of claim 45, wherein the social graph comprises the user'"'"'s actions on Internet-connected nodes external to the social network environment.

49. The system of claim 45, wherein the social graph comprises common keywords in the user'"'"'s messages.

50. The system of claim 43, wherein the attribute nodes comprise metadata tags.

51. The system of claim 50, wherein the metadata tags are received from a node administrator associated with a particular object node.

52. The system of claim 51, wherein the received metadata tags are initially assigned a predetermined feedback weight.

53. The system of claim 39, further comprising promoting nodes with a match coefficient exceeding a predetermined threshold to the particular user.

54. The system of claim 39, further comprising ranking all nodes by descending match coefficient for display to the particular user.

55. The system of claim 39, wherein attribute nodes comprise concept nodes.

56. The system of claim 55, wherein object nodes comprise concept nodes.

57. The system of claim 55, wherein concept nodes are dynamically generated by the social graph.