Determining explicit and implicit user biases for search results on online social networks

• US 10,616,089 B2
• Filed: 12/11/2017
• Issued: 04/07/2020
• Est. Priority Date: 08/01/2014
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Brewer, Jason Eric
• Primary Examiner(s): Mamillapalli, Pavan
• Application Number:
  US15/837,864
• Publication Number:
  US 20180115479A1
• Time in Patent Office: 848 Days
• Field of Search: 707734
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 43/12 Network monitoring probes

Abstract

In one embodiment, a method includes receiving a query input, parsing the query input to identify one or more n-grams, determining a search bias of the first user with respect to the query input, the search bias being determined based on an explicit bias and an implicit bias of the first user, wherein the explicit bias is based on an analysis of the entities associated with the online social networking matching n-grams in the query input, and wherein the implicit bias is based on an analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user, identifying content objects matching the query input based at least in part on the search bias of the first user, and sending instructions for presenting a search-results interface comprising references to the identified content objects.

Claims

1. A method comprising, by one or more computing systems associated with an online social network:

receiving, at the one or more computing systems from a client system associated with a first user of the online social network, a query input comprising a character string;

parsing, by the one or more computing systems, the query input to identify one or more n-grams;

determining, by the one or more computing systems, a search bias of the first user with respect to the query input, the search bias being determined based on an explicit bias and an implicit bias of the first user, wherein the explicit bias is based on an analysis of the entities associated with the online social networking matching n-grams in the query input, and wherein the implicit bias is based on an analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user;

identifying, by the one or more computing systems, one or more content objects matching the query input, wherein the one or more content objects are identified based at least in part on the search bias of the first user; and

sending, from by the one or more computing systems to the client system, responsive to the query input, instructions for presenting a search-results interface, wherein the search-results interface comprises references to one or more of the identified content objects.

2. The method of claim 1, further comprising:

generating a query command in response to receiving the query input, wherein;

the query command comprises one or more query constraints; and

the explicit bias of the first user is determined based at least in part on one or more query constraints of the query command.

3. The method of claim 1, further comprising:

generating a query command in response to receiving the query input, wherein;

the query command comprises one or more query constraints; and

one or more of the query constraints is based at least in part on the determined search bias of the first user.

4. The method of claim 1, wherein the implicit bias is determined based on the analysis of user-profile information of the plurality of second users sharing one or more user attributes with the first user by:

identifying at least one candidate user of the plurality of second users;

comparing at least one first user attribute to at least one second user attribute of the at least one candidate user; and

including, in the plurality of second users, the at least one candidate user when the at least one first user attribute matches the at least one second user attribute.

5. The method of claim 4, wherein the at least one first user attribute comprises at least one first data field,

the at least one second user attribute of the at least one candidate user comprises at least one second data field, and

the at least one first user attribute matches the at least one second user attribute when the at least one first data field matches the at least one second data field.

6. The method of claim 4, wherein the at least one first user attribute comprises at least one first user-bias connected to the first user by at least one first user-bias connection,

the at least one second user attribute of the at least one candidate user comprises at least one second user-bias connected to the at least one candidate user by at least one second user-bias connection, and

the at least one first user attribute matches the at least one second user attribute when the at least one first user-bias matches the at least one second user-bias, and the at least one first user-bias connection matches the at least one second user-bias connection.

7. The method of claim 4, wherein identifying one or more users of the plurality of second users comprises:

identifying at least one biased content object that is connected to at least one of the plurality of second users by one or more connections; and

including the at least one biased content object in the one or more users of the plurality of second users when the at least one biased content object satisfies the query.

8. The method of claim 4, wherein the implicit bias of the first user comprises a field-based user bias that includes at least one biasing user and at least one biasing-connection, and wherein determining the implicit bias based on the analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user further comprises:

when the at least one first user attribute matches the at least one second user attribute;

identifying the at least one biasing user based on the at least one candidate user for which the at least one first user attribute matches the at least one second user attribute, and

identifying the at least one biasing-connection type based on at least one connection that connects the at least one candidate user to at least one biased content object.

9. The method of claim 8, wherein the at least one biasing user includes a copy of or a reference to the at least one candidate user for which the at least one first user attribute matches the at least one second user attribute.

10. The method of claim 8, wherein identifying the one or more users of the plurality of second users comprises:

identifying at least one user from the plurality of second users, wherein the at least one user matches the at least one bias of the implicit bias of the first user and is connected to at least one biased content object by at least one connection of the at least one biasing-connection type; and

including the at least one biased content object in the one or more users of the plurality of second users when the at least one biased content object satisfies the query.

11. The method of claim 4, wherein the implicit bias of the first user comprises an object-based user bias that includes at least one biasing user attribute of a biasing user, and at least one biasing-connection type, and wherein determining the implicit bias based on the analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user further comprises:

when the at least one first user attribute matches the at least one second user attribute;

identifying the at least one biasing user attribute based on the first user attribute that matches the at least one second user attribute, and

identifying the at least one biasing-connection type based on at least one connection that connects the at least one candidate user to at least one biased content object.

12. The method of claim 11, wherein identifying the one or more users of the plurality of second users comprises:

identifying at least one user from the plurality of second users, wherein the at least one user is associated with the at least one biasing user attribute and is connected to at least one biased content object by at least one connection of the at least one biasing-connection type; and

including the at least one biased content object in the one or more users of the plurality of second users when the at least one biased content object satisfies the query.

13. The method of claim 12, wherein determining the implicit bias based on the analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user further comprises:

including, in the biasing user, one or more biasing data fields from the candidate user that represent the at least one first user attribute.

14. The method of claim 13, wherein the one or more users are associated with the at least one biasing user attribute when one or more user data fields included in the one or more users match the one or more biasing data fields included in the biasing user.

15. The method of claim 12, wherein determining the implicit bias based on the analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user further comprises:

connecting, to the biasing user, at least one user-bias that represents the at least one first user attribute, via at least one user-bias connection based on a user-bias connection type based on a type of at least one connection that connects the candidate user to a user-bias, wherein the one or more users are associated with the at least one biasing user attribute when the one or more users are connected to the at least one user-bias via at least one connection of the user-bias connection type.

16. The method of claim 4, wherein the plurality of candidate users comprise the plurality of second users.

17. The method of claim 4, wherein the plurality of candidate users comprise one or more users that are reachable from the first user via one or more user-bias connected by one or more user-bias connections.

18. The method of claim 1, wherein the user attributes include user age, sex, gender, ethnicity, religion, current location, town lived in, home town, likes, friends, school attended, game played, music listened to, video watched, organization worked at, or a combination thereof.

19. The method of claim 1, wherein determining the implicit bias based on the analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user further comprises:

calculating a score for each of the identified users, wherein the score is calculated using a probabilistic ranking model that scores each identified user based at least in part on a number of connections connecting the identified user to one or more users within the first set of users, the first set of users comprising the first user and a plurality of second users, respectively, sharing one or more user attributes with the first user.

20. The method of claim 1, wherein the plurality of second users correspond to a sub-population of an overall population of users associated with the online social network.

21. The method of claim 1, further comprising rewriting the query to include conditions based on the determined search bias of the first user.

22. The method of claim 1, further comprising updating the determined search bias of the first user if one or more of the plurality of second users are removed from the online social network.

23. The method of claim 1, wherein determining the search bias of the first user is further based on a structure of the shared one or more user attributes.

24. The method of claim 1, wherein identifying the one or more content objects further comprises scoring the one or more content objects based on one or more scoring algorithms, wherein the one or more scoring algorithms are customized based on the determined search bias of the first user.

25. The method of claim 1, wherein determining the search bias of the first user is further based on a search history of the first user.

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

receive, at the one or more computing systems from a client system associated with a first user of the online social network, a query input comprising a character string;

parse, by the one or more computing systems, the query input to identify one or more n-grams;

determine, by the one or more computing systems, a search bias of the first user with respect to the query input, the search bias being determined based on an explicit bias and an implicit bias of the first user, wherein the explicit bias is based on an analysis of the entities associated with the online social networking matching n-grams in the query input, and wherein the implicit bias is based on an analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user;

identify, by the one or more computing systems, one or more content objects matching the query input, wherein the one or more content objects are identified based at least in part on the search bias of the first user; and

send, from by the one or more computing systems to the client system, responsive to the query input, instructions for presenting a search-results interface, wherein the search-results interface comprises references to one or more of the identified content objects.

27. 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, at the one or more computing systems from a client system associated with a first user of the online social network, a query input comprising a character string;

parse, by the one or more computing systems, the query input to identify one or more n-grams;

determine, by the one or more computing systems, a search bias of the first user with respect to the query input, the search bias being determined based on an explicit bias and an implicit bias of the first user, wherein the explicit bias is based on an analysis of the entities associated with the online social networking matching n-grams in the query input, and wherein the implicit bias is based on an analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user;

identify, by the one or more computing systems, one or more content objects matching the query input, wherein the one or more content objects are identified based at least in part on the search bias of the first user; and

send, from by the one or more computing systems to the client system, responsive to the query input, instructions for presenting a search-results interface, wherein the search-results interface comprises references to one or more of the identified content objects.