CLUSTER-BASED FRIEND SUGGESTION AGGREGATOR

• US 20100191844A1
• Filed: 01/27/2009
• Published: 07/29/2010
• Est. Priority Date: 01/27/2009
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: He, Lin, Kahn, Jason C, Millar, Luke T.
• Granted Patent: US 8,055,720 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Techniques are described to generate a list of suggested friends for a client of a social networking service. In an implementation, a cluster of clients is identified from among the clients of the social networking service. The cluster of clients includes the client for which the list of suggested friends is to be generated. The list of suggested friends for the client may then be generated and may include one or more clients of the social networking service selected based on connections with the cluster of clients. The list indicates at least one other client of the social networking service that is suggested as a friend.

Claims

1. A computer implemented method comprising:

identifying a cluster of clients selected from a plurality of clients of a social networking service, the cluster of clients including a first client; and

generating a list of suggested friends for the first client, the list of suggested friends including one or more of the plurality of clients that are selected based on connections of the one or more clients with the cluster of clients.

2. A method as recited in claim 1, further comprising ranking the one or more clients based on the connections of the one or more clients with the cluster of clients to generate a ranked suggestion list of the one or more clients, the list of suggested friends being based on the ranked suggestion list.

3. A method as recited in claim 2, further comprising augmenting the ranked suggestion list with a friend suggestion made by a data provider.

4. A method as recited in claim 3, wherein the augmenting of the ranked suggestion list comprises ranking the friend suggestion.

5. A method as recited in claim 4, wherein the ranking of the one or more friend suggestions comprises applying a weighting factor to the friend suggestion, the weighting factor being adjusted based on past acceptance or rejection of friends suggested to the first client by the data provider.

6. A method as recited in claim 1, wherein the identifying of the cluster of clients comprises selecting clients of the plurality of clients to be in the cluster of clients based on whether the clients have connections, one to another.

7. A method as recited in claim 1, wherein the selecting of the clients continues until the cluster of clients includes a predetermined number of clients.

8. One or more computer-readable media comprising instructions that are executable to implement a social networking service that is configured to generate a list of suggested friends for a first client of the social networking service based on connections of one or more additional clients with a cluster of clients.

9. One or more computer readable media as recited in claim 8, wherein the one or more additional clients are ranked based on the connections of the one or more additional clients with the cluster of clients to generate a ranked suggestion list, the list of suggested friends being based on the ranked suggestion list.

10. One or more computer readable media as recited in claim 9, wherein the ranked suggestion list is augmented with one or more friend suggestions made by one or more data providers.

11. One or more computer readable media as recited in claim 10, wherein the one or more friend suggestions are weighted with at least one weighting factor.

12. One or more computer readable media as recited in claim 11, wherein the at least one weighting factor is adjusted based on past acceptance or rejection of friends suggested to the first client by the one or more data providers.

13. One or more computer readable media as recited in claim 8, wherein the cluster of clients is identified by selecting clients of the plurality of clients to be in the cluster of clients based on whether the clients have connections to the cluster of clients.

14. One or more computer readable media as recited in claim 8, wherein clients of the plurality of clients are selected to be in the cluster of clients until the cluster of clients includes a predetermined number of clients.

15. A system comprising one or more modules having instructions that are executable to:

select a cluster of clients from a plurality of clients of a social networking service, the cluster of clients including a first client;

rank one or more said clients of the plurality of clients based on connections of the one or more said clients with the cluster to create a ranking of the one or more said clients; and

generate a list of suggested friends for the first client based on the ranking of the one or more said clients, the list indicating at least one of the one or more said clients that is suggested as a friend to the first client.

16. A system as recited in claim 15, wherein the ranking of the one or more said clients is augmented with one or more friend suggestions made by one or more data providers.

17. A system as recited in claim 16, wherein the one or more friend suggestions are weighted with at least one weighting factor.

18. A system as recited in claim 17, wherein the at least one weighting factor is adjusted based on past acceptance or rejection of friends suggested to the first client by the one or more data providers.

19. A system as recited in claim 15, wherein the cluster is identified by selecting clients of the plurality of clients to be in the cluster, the selected clients having connections to the cluster.

20. A system as recited in claim 15, wherein clients of the plurality of clients are selected to be in the cluster until the cluster includes a predetermined number of said clients.