Soft matching user identifiers

• US 9,154,575 B2
• Filed: 08/28/2012
• Issued: 10/06/2015
• Est. Priority Date: 08/28/2012
• Status: Expired due to Fees

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Harik, Hisham R., Sidhu, Kiranjit S., Xu, Hao, Lopez, Javier Olivan
• Primary Examiner(s): Luu, Le H
• Application Number:
  US13/597,126
• Publication Number:
  US 20140067931A1
• Time in Patent Office: 1,134 Days
• Field of Search: 709/204, 707/769, 707/771, 707/727, 707/748, 707/759, 707/732, 345/660
• US Class Current: 1/1
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06Q 30/01 Customer relationship services
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04W 4/80 Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

Particular embodiments may receive one or more data items associated with a first user of a social-networking system. The one or more data items being related to an entity. One or more data stores may store a social graph of the social-networking system. The social graph may comprise a plurality of nodes and plurality of edges between nodes. The nodes may comprise user nodes corresponding to users of the social-networking system and concept nodes corresponding to concepts. Particular embodiments may identify one or more nodes of the social graph that likely match the entity and associate at least one of the data items to at least one of the identified nodes.

Claims

1. A method comprising:

receiving a plurality of data items imported into a social-networking system by a first user of the social-networking system, the plurality of data items being related to an entity;

accessing, by one or more processors associated with one or more computer servers associated with the social-networking system, one or more data stores storing a social graph of the social-networking system, the social graph comprising a plurality of nodes and a plurality of edges between nodes, the nodes comprising user nodes corresponding to users of the social-networking system and concept nodes corresponding to concepts;

identifying, by the one or more processors, one or more nodes of the social graph that likely match the entity, each of the one or more nodes having at least one matching attribute that matches one of the plurality of data items; and

updating, by the one or more processors, an unmatched attribute, different from the at least one matching attribute, of at least one of the identified nodes with at least one of the data items.

2. The method of claim 1, wherein the data items comprise an email address, a name, a phone number, a user identifier of the social-networking system, a unique identifier of a communication channel or application, or a device identifier.

3. The method of claim 1, wherein the data items comprise a cookie or token associated with a user of the social-networking system.

4. The method of claim 1, wherein the data items comprise contact information imported to the social-networking system.

5. The method of claim 1, wherein the identified nodes are within a specified degree of separation from a user node corresponding to the first user in the social graph.

6. The method of claim 1, wherein the identifying one or more nodes of the social graph that likely match the entity is further based on a respective measure of affinity between the nodes and the first user.

7. The method of claim 1, wherein the identifying one or more nodes of the social graph that likely match the entity further comprises:

comparing respective name text strings of the nodes to a name text string of the entity based on a Levenshtein distance algorithm.

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

receive a plurality of data items imported into a social-networking system by a first user of the social-networking system, the plurality of data items being related to an entity;

access, by one or more processors associated with one or more computer servers associated with the social-networking system, one or more data stores storing a social graph of the social-networking system, the social graph comprising a plurality of nodes and a plurality of edges between nodes, the nodes comprising user nodes corresponding to users of the social-networking system and concept nodes corresponding to concepts;

identify, by the one or more processors, one or more nodes of the social graph that likely match the entity, each of the one or more nodes having at least one matching attribute that matches one of the plurality of data items; and

update, by the one or more processors, an unmatched attribute, different from the at least one matching attribute, of at least one of the identified nodes with at least one of the data items.

9. The media of claim 8, wherein the data items comprise an email address, a name, a phone number, a user identifier of the social-networking system, a unique identifier of a communication channel or application, or a device identifier.

10. The media of claim 8, wherein the data items comprise a cookie or token associated with a user of the social-networking system.

11. The media of claim 8, wherein the data items comprise contact information imported to the social-networking system.

12. The media of claim 8, wherein the identified nodes are within a specified degree of separation from a user node corresponding to the first user in the social graph.

13. The media of claim 8, wherein the identification of one or more nodes of the social graph that likely match the entity is further based on a respective measure of affinity between the nodes and the first user.

14. The media of claim 8, wherein the identification of one or more nodes of the social graph that likely match the entity further comprises:

comparing respective name text strings of the nodes to a name text string of the entity based on a Levenshtein distance algorithm.

15. A system comprising:

one or more processors; and

a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;

receive a plurality of data items associated with imported into a social-networking system by a first user of the social-networking system, the plurality of data items being related to an entity;

access one or more data stores storing a social graph of the social-networking system, the social graph comprising a plurality of nodes and a plurality of edges between nodes, the nodes comprising user nodes corresponding to users of the social-networking system and concept nodes corresponding to concepts;

identify one or more nodes of the social graph that likely match the entity, each of the one or more nodes having at least one matching attribute that matches one of the plurality of data items; and

update an unmatched attribute, different from the at least one matching attribute, of at least one of the identified nodes with at least one of the data items.

16. The system of claim 15, wherein the data items comprise an email address, a name, a phone number, a user identifier of the social-networking system, a unique identifier of a communication channel or application, or a device identifier.

17. The system of claim 15, wherein the data items comprise (1) a cookie or token associated with a user of the social-networking system, (2) contact information imported to the social-networking system, or any combination thereof.

18. The system of claim 15, wherein the identified nodes are within a specified degree of separation from a user node corresponding to the first user in the social graph.

19. The system of claim 15, wherein the identification of one or more nodes of the social graph that likely match the entity is further based on a respective measure of affinity between the nodes and the first user.

20. The system of claim 15, wherein the identification of one or more nodes of the social graph that likely match the entity further comprises:

comparing respective name text strings of the nodes to a name text string of the entity based on a Levenshtein distance algorithm.