Identifying interest twins in an online community

• US 8,166,058 B2
• Filed: 12/28/2006
• Issued: 04/24/2012
• Est. Priority Date: 12/28/2006
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Xu, Hao, Xu, Zhichen
• Primary Examiner(s): COLAN, GIOVANNA B
• Application Number:
  US11/647,938
• Publication Number:
  US 20080162431A1
• Time in Patent Office: 1,944 Days
• Field of Search: 707/705, 707/821, 707/758, 707/706, 707/722, 707/736, 707/102, 707/5, 707/103.Y, 707/10, 707/2, 707/7, 709/217
• US Class Current: 707/758
• CPC Class Codes: G06F 16/957 Browsing optimisation, e.g. caching or content distillation
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

Techniques are described for identifying one or more “interest twins” of a user. An interest twin of a user in another user that has demonstrated interests in items that are the same as or similar to the items in which the user has demonstrated an interest. Various techniques are described for reducing the overhead in interest twin determination operations. Once the interest twins for a user have been identified, the knowledge of the interest twins may be used in a variety of ways to enhance to experience of the user. For example, a mechanism may be provided which allows the user to see a list of items in which the user'"'"'s interest twins have indicated an interest.

Claims

1. A computer implemented method for identifying interest twins of a target user, the method comprising:

recording interest-events performed by members of an online community that includes said target user;

identifying a first item and a second item, in which the target user is interested;

generating a first individual item score for each member of a first particular set of members of the online community based, at least in part, on the amount of interest that said each member of the first particular set has demonstrated in the first item;

generating a second individual item score for each member of a second particular set of members of the online community based, at least in part, on the amount of interest that said each member of the second particular set has demonstrated in the second item;

determining a first set of one or more interest twins for the target user based, at least in part, on the first individual item scores generated for the first particular set of members;

determining a second set of one or more interest twins for the target user based, at least in part, on the second individual item scores generated for the second particular set of members wherein the first set of interest twins is not the same as the second set of interest twins;

wherein determining the first set of one or more interest twins for the target user includes generating twin scores for a plurality of members of the online community based on the first individual item scores that were generated for the plurality of members;

wherein the first set of one or more interest twins for the target user are selected from the plurality of members based on their respective twin scores;

wherein the method is performed by one or more computing devices.

2. The method of claim 1 wherein:

the items belong to categories; and

the individual item score, for an item, for each member of the first particular set of members, is based, at least in part, on the amount of interest that said member has demonstrated in items that belong to the category to which the item belongs.

3. The method of claim 2 wherein the individual item score, for an item, for each member of the first particular set of members, is based, at least in part, on the amount of interest that said member has demonstrated in items that belong to categories that are related to the category to which the item belongs.

4. The method of claim 1 wherein the step of determining a set of one or more interest twins for the target user includes:

generating composite scores based on said individual item scores;

ranking members of the online community based on the composite scores; and

selecting the members that have the N highest ranks to be the interest twins for the target user.

5. The method of claim 1 wherein the step of determining a first particular set of one or more interest twins for the target user is based, at least in part, on which members of the online community are in a social network of the target user.

6. The method of claim 1 wherein the step of determining a first particular set of one or more interest twins for the target user is based, at least in part, on reputation values associated with members of the online community.

7. The method of claim 1 further comprising determining a subset of members of the online community for which to determine interest twins based, at least in part, on the size of social networks associated with members of the online community.

8. The method of claim 1 further comprising determining a subset of members of the online community for which to determine interest twins based, at least in part, on the amount of interest-actions performed by members of the online community.

9. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 1.

10. The method of claim 1, wherein the step of recording interest events is performed in response to detecting a member performing at least one of a) creating a tag;

b) creating a bookmark;

or c) selecting another member of the online community as a friend.

11. The method of claim 1, wherein the first user has demonstrated less interest in a particular item than the second user has demonstrated in the particular item.

12. The method of claim 1, wherein determining a set of interest twins for the target user includes:

identifying a particular set of items in which the target user is interested;

creating a set of target items for the target user by selecting a subset of the particular set of items in which the target user is interested;

wherein selecting the subset is performed based, at least in part, on how much interest the target user has demonstrated in the items that belong to the particular set of items; and

selecting, from among other members of the online community, a set of one or more interest twins for the target user based on how much interest said other members of the online community demonstrated in the target items without regard to how much interest said other members had in items that (a) belong to said particular set but (b) have not been selected as target items.

13. The method of claim 1, wherein determining a set of interest twins for the target user includes:

identifying a set of target items in which the target user is interested;

for each target item in the set of target items;

creating a comparison set for the first target user for the item by;

selecting a size for the comparison set for the target user for the target item; and

based on the selected size, selecting a subset of members of the online community that have demonstrated an interest in the target item to include in the comparison set for the target user for the target item;

performing comparisons between (a) the target user'"'"'s interest in the item, and (b) only the interests in the item that were demonstrated by members of the online community that were selected to be included in the comparison set for the target user for the target item;

determining a set of one or more interest twins for the target user based, at least in part, on the comparisons.

14. The method of claim 1, wherein determining a set of interest twins for the target user includes:

identifying a particular set of items in which the target user is interested;

wherein a subset of members of the online community belong to a social network of the target user;

determining a set of one or more interest twins for the target user based, at least in part, on;

a) how much interest other members of the online community have demonstrated in the particular set of items; and

b) which members of the online community are in the social network of the target user.

15. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 2.

16. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 3.

17. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 4.

18. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 5.

19. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 6.

20. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 7.

21. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 8.

22. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 10.

23. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 11.

24. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 12.

25. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 13.

26. The method of claim 14, wherein the set of one or more interest twins for the target user includes at least one member that is not in the social network of the target user.

27. The method of claim 14, further comprising:

wherein the social network establishes a social network distance between the target user and each member of the social network of the target user;

wherein the step of determining a set of one or more interest twins for the target user is based, at least in part, on the social network distance between the target user and one or more members of the first social network.

28. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 14.

29. The method of claim 4 further comprising:

storing data that indicates the members with the M highest ranks, where M is greater than N; and

considering only the members indicated in the stored data when performing a subsequent interest twin determination operation for the target user.

30. The method of claim 12 wherein:

the items are tags; and

the method further comprises determining the general popularity of the tags based, at least in part, on how many users have used the tags, and how many taggable items have been tagged with the tags.

31. The method of claim 13 further comprising selecting how many members to include in the comparison set based, at least in part, on how much interest the target user has demonstrated in the item for which the comparison set is generated.

32. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 26.

33. The method of claim 27, further comprising:

adjusting an individual item score or a composite tag twin score between the target user and a particular member of the target user'"'"'s social network based on the social network distance between the target user and the particular member of the social network of the target user.

34. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 27.

35. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 29.

36. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 30.

37. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 31.

38. The method of claim 13 further comprising selecting how many members to include in the comparison set based, at least in part, on a general popularity level of the item for which the comparison set is generated.

39. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 33.

40. The method of claim 38 further comprising:

establishing a relatively low maximum membership for comparison sets generated for relatively popular items; and

establishing a relatively high maximum membership for comparison sets generated for relatively unpopular items.

41. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 38.

42. A machine-readable volatile or non-volatile medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 40.