AUTOMATIC UPDATING OF TRUST NETWORKS IN RECOMMENDER SYSTEMS

• US 20130097184A1
• Filed: 12/10/2012
• Published: 04/18/2013
• Est. Priority Date: 09/15/2004
• Status: Abandoned Application

• Current Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Berkhin, Pavel, Maghoul, Farzin
• Application Number:
  US13/709,764
• Publication Number:
  US 20130097184A1
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results

Abstract

Trust networks in a recommender system are automatically updated in response to user feedback on recommendations provided by the trust network. In response to a user request, a set of referrals is generated, with some of the referrals being recommended based on judgment data received from members of the trust network. If the user evaluates the recommended referral, a trust parameter for at least one of the trust network members is updated based on the evaluation.

Claims

1-29. -29. (canceled)

30. A computerized method for maintaining a trust network for a recommender system, the method comprising:

electronically defining, via a processing device, a trust network of direct and indirect relationships among a plurality of trust network members, each direct relationship having a trust weight;

receiving judgment data of content items for the plurality of trust network members having a trust weight value associated therewith that is above a threshold value;

in response to a search request from a user, electronically generating a list of given members from the plurality of trust network members via the processing device, wherein the requesting user is one of the plurality of trust network members and wherein the list of given members includes at least one of the plurality of trust network members having the trust weight value above the threshold value;

electronically selecting one or more of the given members based on the received judgment data and respective confidence coefficients for at least some of the plurality of trust network members, wherein the confidence coefficients are computed based on the trust weights of the direct relationships in the trust network, wherein selection of the one or more given members is based on the network members that have a direct relationship with the user;

presenting one or more rated content items of the selected given members according to the received judgment data and respective confidence coefficients to the requesting user of the search request;

receiving from the requesting user an evaluation of the rating of the one or more rated content items; and

updating the trust weight of at least one of the relationships between the requesting user and another member of the trust network based on the evaluation of the rating of the one or more rated content items.

31. The method of claim 30, wherein presenting the one or more rated content items includes:

updating the trust network to indicate that the one or more selected given members are suggested as new friends; and

presenting the updated trust network to the user.

32. The method of claim 30, wherein selecting the one or more of the given members includes computing a score for each of the plurality of trust network members.

33. The method of claim 32, wherein the judgment data includes a numerical rating and wherein computing the score includes:

weighting the numerical rating given by each of the plurality of trust network members by the respective confidence coefficient for that trust network member, and

adding the weighted numerical ratings.

34. The method of claim 30, wherein the relationship between the requesting user and at least one of the trust network members is an indirect relationship.

35. The method of claim 34, wherein the confidence coefficient associated with each indirectly related trust network member is determined based on a sequence of direct relationships linking the requesting user to the indirectly related trust network member.

36. The method of claim 35, wherein the confidence coefficients are determined using a portfolio allocation model.

37. The method of claim 30, wherein updating the trust weight includes:

identifying as a friend each member of the trust network that has a direct relationship with the requesting user;

computing a gain function for each friend;

selecting a friend based on the gain function; and

computing a new trust weight for the selected friend.

38. The method of claim 37, wherein the gain function is based at least in part on judgment data provided by the friend.

39. The method of claim 37, wherein the gain function is based at least in part on judgment data provided by a trust network member directly or indirectly related to the friend.

40. The method of claim 37, wherein the gain function is based in part on the trust weight of the relationship between the requesting user and the friend.

41. The method of claim 37, wherein the new trust weight is computed according to a history-dependent update function.

42. The method of claim 30, further comprising proposing, based on the evaluation, a new direct relationship between the requesting user and one of the trust network members.

43. The method of claim 42, wherein proposing a new direct relationship includes:

identifying as a candidate at least one trust network member that is not directly related to the requesting user;

computing a gain function for each candidate;

determining, based on the gain functions, whether to propose a direct relationship between the requesting user and the candidate; and

transmitting information about the proposed direct relationship to the requesting user.

44. The method of claim 42, further comprising:

adding the proposed direct relationship to the trust network in response to an acceptance of the proposed direct relation by the requesting user.

45. The method of claim 30, further comprising:

computing an updated global authority score for at least one member of the trust network, the updated global authority score indicating a level of trust for the at least one member of the trust network with a community of trust network members, wherein the updated global authority score is based at least in part on the updated trust weight.

46. A computer-based recommender system comprising:

a trust data store configured to store a trust network of direct and indirect relationships among a plurality of trust network members, each direct relationship having a trust weight;

a judgment data store configured to store judgment data of content items, the judgment data being received from the plurality of trust network members having a trust weight value associated therewith that is above a threshold value;

a request processing module configured to receive a search request from a requesting user, the requesting user being one of the plurality of trust network members, and to generate a list of given members from the plurality of trust network members in response to the request, the list of given members including at least one of the plurality of trust network members having the trust weight value above the threshold;

the request processing module being further configured to select one or more of the given members based on the judgment data in the judgment data store and respective confidence coefficients associated with each of the trust network members, wherein the confidence coefficients are computed based on the trust weights of the direct relationships in the trust network, wherein selection of the one or more given members is based on the network members having a direct relationship with the user;

a transaction module configured to present one or more rated content items of the selected given members according to the received judgment data and respective confidence coefficients to the requesting user of the search request and to receive from the requesting user an evaluation of the rating of the one or more rated content items; and

an update module configured to update the trust weight of at least one of the relationships between the requesting user and another member of the trust network based on the evaluation of the one or more rated content items.

47. The system of claim 46, wherein the judgment data includes numerical ratings and wherein the request processing module is further configured to use the numerical ratings to compute a score for each of the plurality of trust network members and to select the one or more rated content items based on the respective scores of the trust network members.

48. The system of claim 46, wherein the update module is further configured to identify as a friend each member of the trust network that has a direct relationship with the requesting user, to compute a gain function for each friend, to select a friend based on the gain function, and to compute a new trust weight for the selected friend.

49. The system of claim 48, wherein the update module is further configured to:

identify as a candidate at least one trust network member that is not directly related to the requesting user;

compute a gain function for each candidate;

determine, based on the gain functions, whether to propose a direct relationship between the requesting user and the candidate;

transmit information about the proposed direct relationship to the requesting user; and

add the proposed direct relationship to the trust network in response to an acceptance of the proposed direct relation by the requesting user.