Ranking of Address Book Contacts Based on Social Proximity

• US 20150019483A1
• Filed: 10/01/2014
• Published: 01/15/2015
• Est. Priority Date: 12/17/2010
• 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
• Granted Patent: US 9,396,288 B2
• Time in Patent Office: Days
• US Class Current: 707/610
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/27 Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/90348 by searching ordered data, e.g. alpha-numerically ordered data
• 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 16/9538 Presentation of query results
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 61/4594 Address books, i.e. directories containing contact information about correspondents telephone directories in user terminals H04M1/27453
• CPC Class Codes: H04L 67/01 Protocols

Abstract

In one embodiment, a user of a social networking system requests to look up an address book maintained by the social networking system. The social networking system improves the look up search results by ranking one or more contacts in the address book based on social graph, social relationship and communication history information.

Claims

1. A method comprising:

by one or more computing devices, accessing a contacts list comprising one or more contacts in an address book of a first user;

by one or more computing devices, accessing a data store for existing social-graph information between each contact in the contacts list and the first user;

by one or more computing devices, accessing communication-history information for each contact in the contacts list, the communication-history information for a contact indicating a frequency of communication between the first user and the contact;

by one or more computing devices, determining a social-proximity score for each contact in the contacts list based at least in part on the social-graph information, communication-history information, and one or more user-declared relationship types between the first user and other contacts of the contacts list;

by one or more computing devices, sending the social-proximity scores for the contacts in the contacts list to a client device associated with the first user, wherein a ranked list of the contacts displayed on the client device is determined based at least in part on the social-proximity scores.

2. The method of claim 1, wherein the social-graph information comprises a degree-of-separation coefficient for each contact in the contacts list with respect to the first user.

3. The method of claim 1, wherein the social-graph information comprises an affiliation coefficient for each contact in the contacts list with respect to the first user.

4. The method of claim 1, wherein accessing the communication-history information for each contact in the contacts list comprises:

accessing a data store of communication history; and

determining a usage frequency for each of one or more communication channels for the contact.

5. The method of claim 4, wherein the social-graph information comprises (1) a degree-of-separation coefficient for each contact in the contacts list with respect to the first user and (2) an affiliation coefficient for each contact in the contacts list with respect to the first user.

6. The method of claim 5, the method further comprising:

accessing a weighting factor for each of the communication channels, wherein the social-proximity score for each contact in the contacts list is based on a summation of the degree-of-separation coefficient, the affiliation coefficient, and the weighted average of the usage frequency of the communication channels, and wherein the weighted average is calculated with the weighting factor for each of the communication channels.

7. The method of claim 1, the method further comprising periodically synchronizing a local copy of the social-proximity scores with a server-side copy of the social-proximity scores by:

polling the client device to determine changes to the local copy since the copies of the social-proximity scores were last synchronized;

determining changes to the server-side copy since the copies of the social-proximity scores were last synchronized;

updating the server-side copy based on the determined changes to the local copy; and

sending information to the client device to update the local copy based on the determined changes to the server-side copy.

8. 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 a contacts list comprising one or more contacts in an address book of a first user;

access a data store for existing social-graph information between each contact in the contacts list and the first user;

access communication-history information for each contact in the contacts list, the communication-history information for a contact indicating a frequency of communication between the first user and the contact;

determine a social-proximity score for each contact in the contacts list based at least in part on the social-graph information, a-weighted average of the usage frequency of each of the communication channels, and one or more user-declared relationship types between the first user and other contacts of the contacts list;

by one or more computing devices, sending the social-proximity scores for the contacts in the contacts list to a client device associated with the first user, wherein a ranked list of the contacts displayed on the client device is determined based at least in part on the social-proximity scores.

9. The system of claim 8, wherein the social-graph information comprises a degree-of-separation coefficient for each contact in the contacts list with respect to the first user.

10. The system of claim 8, wherein the social-graph information comprises an affiliation coefficient for each contact in the contacts list with respect to the first user.

11. The system of claim 8, wherein the processors being operable when executing the instructions to access the communication-history information for each contact in the contacts list comprises the processors being operable to:

access a data store of communication history; and

determine a usage frequency for each of one or more communication channels for the contact.

12. The system of claim 11, wherein the social-graph information comprises (1) a degree-of-separation coefficient for each contact in the contacts list with respect to the first user and (2) an affiliation coefficient for each contact in the contacts list with respect to the first user.

13. The system of claim 12, wherein the processors are further operable when executing the instructions to:

access a weighting factor for each of the communication channels, wherein the social-proximity score for each contact in the contacts list is based on a summation of the degree-of-separation coefficient, the affiliation coefficient, and the weighted average of the usage frequency of the communication channels, and wherein the weighted average is calculated with the weighting factor for each of the communication channels.

14. The system of claim 8, wherein the processors are further operable when executing the instructions to periodically synchronize a local copy of the social-proximity scores with a server-side copy of the social-proximity scores by:

polling the client device to determine changes to the local copy since the copies of the social-proximity scores were last synchronized;

determining changes to the server-side copy since the copies of the social-proximity scores were last synchronized;

updating the server-side copy based on the determined changes to the local copy; and

sending information to the client device to update the local copy based on the determined changes to the server-side copy.

15. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

access a contacts list comprising one or more contacts in an address book of a first user;

access a data store for existing social-graph information between each contact in the contacts list and the first user;

access communication-history information for each contact in the contacts list, the communication-history information for a contact indicating a frequency of communication between the first user and the contact;

determine a social-proximity score for each contact in the contacts list based at least in part on the social-graph information, a weighted average of the usage frequency of each of the communication channels, and one or more user-declared relationship types between the first user and other contacts of the contacts list;

send the social-proximity scores for the contacts in the contacts list to a client device associated with the first user, wherein a ranked list of the contacts displayed on the client device is determined based at least in part on the social-proximity scores.

16. The media of claim 15, wherein the social-graph information comprises a degree-of-separation coefficient for each contact in the contacts list with respect to the first user.

17. The media of claim 15, wherein the social-graph information comprises an affiliation coefficient for each contact in the contacts list with respect to the first user.

18. The media of claim 15, wherein the software operable when executed to access the communication-history information for each contact in the contacts list comprises software that is operable when executed to:

access a data store of communication history; and

determine a usage frequency for each of one or more communication channels for the contact.

19. The media of claim 18, wherein the social-graph information comprises (1) a degree-of-separation coefficient for each contact in the contacts list with respect to the first user and (2) an affiliation coefficient for each contact in the contacts list with respect to the first user, and wherein the software is further operable when executed to:

access a weighting factor for each of the communication channels, wherein the social-proximity score for each contact in the contacts list is based on a summation of the degree-of-separation coefficient, the affiliation coefficient, and the weighted average of the usage frequency of the communication channels, wherein the weighted average is calculated with the weighting factor for each of the communication channels.

20. The media of claim 15, wherein the software is further operable when executed to periodically synchronize a local copy of the social-proximity scores with a server-side copy of the social-proximity scores by:

polling the client device to determine changes to the local copy since the copies of the social-proximity scores were last synchronized;

determining changes to the server-side copy since the copies of the social-proximity scores were last synchronized;

updating the server-side copy based on the determined changes to the local copy; and

sending information to the client device to update the local copy based on the determined changes to the server-side copy.