Group Recommendations in Social Networks

• US 20110270774A1
• Filed: 04/30/2010
• Published: 11/03/2011
• Est. Priority Date: 04/30/2010
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Peres, Yuval, Wilson, David B., Tennenholtz, Moshe, Varshavsky, Roy
• Application Number:
  US12/772,083
• Publication Number:
  US 20110270774A1
• Time in Patent Office: Days
• US Class Current: 705/319
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 63/126 the source of the received data
• 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: H04M 2203/655 Combination of telephone service and social networking messaging within social networks H04L51/52
• CPC Class Codes: H04M 7/0024 Services and arrangements where telephone services are combined with data services arrangements which combine a telephonic equipment and a computer H04M7/0012

Abstract

Providing a recommendation to a group of networked members is disclosed. The recommendation is provided to the group collectively, and is based on trust relationships between the members of the network. In an example embodiment, the network is a social network. Example systems and methods include a two-phase approach and a one-phase approach, each including analysis and aggregation of input associated with members of the network.

Claims

1. A system for providing a group recommendation to a group of users collectively, the group of users comprising a subset of a social network, the system comprising:

a processor;

memory coupled to the processor;

an analysis component stored in the memory and operable on the processor to produce a plurality of individual recommendations for an item, based on trust relationships between members of the group of users and members of the social network that are not members of the group of users; and

an aggregation component stored in the memory and operable on the processor to aggregate the plurality of individual recommendations to produce the group recommendation.

2. The system of claim 1, wherein the members of the social network are represented in a graph as nodes and the trust relationships are represented in the graph as edges between the nodes.

3. The system of claim 2, wherein the graph is a Directed Acyclic Graph (DAG).

4. The system of claim 1, wherein the analysis component is configured to produce the plurality of individual recommendations based on voting behavior of the members of the social network.

5. The system of claim 1, wherein the analysis component is configured to produce the plurality of individual recommendations based on a statistical system of Majority-of-Majorities (MOM).

6. The system of claim 1, wherein the analysis component is configured to produce the plurality of individual recommendations based on a Random Walk System (RW).

7. The system of claim 1, wherein the analysis component is configured to produce the plurality of individual recommendations based on a Min-cut system (mincut).

8. One or more computer readable media comprising computer executable instructions that, when executed by a computer processor, direct the computer processor to provide a recommendation to a group of users collectively, the group of users comprising a subset of a social network, the providing comprising:

designating trust relationships between members of the social network;

collecting input from members of the social network regarding an item;

analyzing, by a computer processor, the input in conjunction with the designated trust relationships;

producing, by the computer processor, individual recommendations based on the analyzing;

aggregating, by the computer processor, the individual recommendations to produce an aggregated recommendation; and

outputting the aggregated recommendation to the group.

9. The one or more computer readable media of claim 8, wherein the designating comprises representing the members of the social network as graph nodes and representing the trust relationships as graph edges.

10. The one or more computer readable media of claim 9, wherein the graph is a Directed Acyclic Graph (DAG).

11. The one or more computer readable media of claim 9, wherein the designating comprises assigning weights to the edges based on the trust relationships.

12. The one or more computer readable media of claim 9, wherein the graph is undirected such that relations between nodes are symmetric.

13. The one or more computer readable media of claim 8, wherein the input is collected only from members of the social network not in the group.

14. A computer implemented method of providing a recommendation to a group of users collectively, the group of users comprising a subset of a social network, the method comprising:

designating trust relationships between members of the social network;

collecting input from members of the social network regarding an item;

analyzing, by a computer processor, the input in conjunction with the designated trust relationships;

aggregating, by the computer processor, the input based on the analyzing and an aggregation algorithm;

producing, by the computer processor, a group recommendation based on the analyzing and the aggregating; and

outputting the group recommendation to the subset.

15. The method of claim 14, wherein at least one of the analyzing, aggregating, and producing further comprises using a Markov Random Field (MRF), where a graph of the MRF represents the trust relationships between the members of the social network.

16. The method of claim 14, wherein the designating comprises representing the members of the social network as graph nodes and representing the trust relationships as graph edges.

17. The method of claim 16, wherein the graph is a Directed Acyclic Graph (DAG).

18. The method of claim 16, wherein the designating comprises assigning weights to the edges and/or the nodes based on the trust relationships.

19. The method of claim 14, further comprising evaluating the structure of the social network;

wherein the analyzing and/or aggregating are based on the evaluating.

20. The method of claim 14, further comprising representing the social network as an Ising system.