Suggested Queries Based on Interaction History on Online Social Networks

• US 20170308583A1
• Filed: 04/20/2016
• Published: 10/26/2017
• Est. Priority Date: 04/20/2016
• Status: Abandoned Application

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Husain, Aliasgar Mumtaz, Jeon, Sung-eok
• Application Number:
  US15/133,620
• Publication Number:
  US 20170308583A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking

Abstract

In one embodiment, a method includes receiving, from a user of an online social network, a text query comprising one or more n-grams inputted by the user. The method also includes identifying a first set of candidate keyword phrases matching the one or more n-grams of the text query, where each candidate keyword phrase in the first set includes one or more n-grams extracted from content associated with a third-party content object interacted with by the user. The method also includes calculating a rank for each of the identified candidate keyword phrases based at least in part on a social-interaction history of the user and sending, to the user in response to the user inputting the one or more n-grams of the text query, one or more suggested queries, where at least one of the suggested queries includes one of the identified candidate keyword phrases.

Claims

1. A method comprising, by one or more computing devices:

receiving, from a client system of a first user of an online social network, a text query comprising one or more n-grams inputted by the first user;

identifying a first set of candidate keyword phrases matching the one or more n-grams of the text query, wherein each candidate keyword phrase in the first set comprises one or more n-grams extracted from content associated with a third-party content object interacted with by the first user;

calculating a rank for each of the identified candidate keyword phrases based at least in part on a social-interaction history of the first user; and

sending, to the client system of the first user for display in response to the first user inputting the one or more n-grams of the text query, one or more suggested queries, wherein at least one of the suggested queries comprises one of the identified candidate keyword phrases associated with a third-party content object having a rank higher than a threshold rank.

2. The method of claim 1, further comprising:

accessing a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising;

a first node corresponding to the first user of the online social network;

a plurality of user nodes corresponding to a plurality of second users of the online social network, respectively; and

a plurality of content nodes corresponding to a plurality of third-party content objects, respectively.

3. The method of claim 1, wherein the third-party content object is stored in a third-party system.

4. The method of claim 1, further comprising identifying a second set of candidate keyword phrases matching the one or more n-grams of the text query, wherein each candidate keyword phrase in the second set comprises one or more n-grams extracted from content associated with a native content object interacted with by the first user, the native content object being stored in a data store associated with the online social network.

5. The method of claim 1, wherein the first user interacting with a third-party content object comprises one or more of:

accessing the third-party content object via a link on the online social network;

posting, to the online social network, a link to the third-party content object;

accessing a content object of the online social network associated with the third-party content object;

commenting on a content object of the online social network associated with the third-party content object;

liking a content object of the online social network associated with the third-party content object;

sharing a content object of the online social network associated with the third-party content object;

or

accessing a search result from the online social network, wherein the search result references the third-party content object.

6. The method of claim 1, further comprising:

extracting, from content associated with a third-party content object, one or more n-grams via a machine-learning algorithm;

generating one or more candidate keyword phrases based on the extracted n-grams; and

storing the generated candidate keyword phrases in association with the third-party content object.

7. The method of claim 6, wherein storing the generated candidate keyword phrases in association with the third-party content object comprises storing the candidate keyword phrases in one or more data stores associated with the online social network.

8. The method of claim 6, wherein storing the generated candidate keyword phrases in association with the third-party content object comprises storing the candidate keyword phrases on a local cache of the client system of the first user.

9. The method of claim 6, wherein the candidate keyword phrases are pre-generated by an auto-suggestion system prior to the first user interacting with one or more third-party content obj ects.

10. The method of claim 1, wherein the content associated with a third-party content object comprises on or more of:

text of the third-party content object;

text of another content object determined to be similar to or of the same category as the third-party content object;

a descriptive tag associated with the third-party content object;

text of a content object of the online social network associated with the third-party content object;

or

a search query associated with the third-party content object.

11. The method of claim 1, wherein the first user interacted with each third-party content object within a specified timeframe.

12. The method of claim 1, wherein the social-interaction history of the first user comprises one or more online interactions of the first user, wherein the online interactions comprise one or more of:

accessing a third-party content object via a link on the online social network;

posting, to the online social network, a link to a third-party content object;

accessing a content object of the online social network associated with a third party content object;

commenting on a content object of the online social network associated with a third-party content object;

liking a content object of the online social network associated with a third-party content object;

sharing a content object of the online social network associated with a third-party content object;

or

accessing a search result from the online social network wherein the search result references a third-party content object.

13. The method of claim 12, wherein the rank for each identified candidate keyword phrase is further based on a time decay factor associated with an online interaction of the first user associated with the content object from which the n-grams corresponding to the candidate keyword phrase were extracted.

14. The method of claim 1, wherein the social-interaction history of the first user comprises clickstream data of the first user, the clickstream data comprising information about one or more online interactions of the first user with one or more third-party content objects.

15. The method of claim 1, wherein calculating the rank for each identified candidate keyword phrase is further based on a social-interaction history of a friend of the first user on the online social network or a user of the online social network determined to be similar to the first user.

16. The method of claim 1, wherein calculating the rank for each identified candidate keyword phrase is further based on analysis of the candidate keyword phrase according to a language model.

17. The method of claim 1, wherein calculating the rank for each identified candidate keyword phrase comprises:

determining that a first candidate keyword phrase comprises an n-gram appearing in content associated with more than one third-party content objects interacted with by the first user;

calculating a number of third-party content objects interacted with by the first user that comprise the n-gram; and

up-ranking the first candidate keyword phrase based on the calculated number of third-party content objects.

18. The method of claim 1, wherein one or more of the suggested queries sent to the client system of the first user comprise one or more keyword phrases generated based on one or more of:

a name of a user or an entity on the online social network;

a language database;

a list of trending-topic keyword phrases;

or

a search history associated with the first user.

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

receive, from a client system of a first user of an online social network, a text query comprising one or more n-grams inputted by the first user;

identify a first set of candidate keyword phrases matching the one or more n-grams of the text query, wherein each candidate keyword phrase in the first set comprises one or more n-grams extracted from content associated with a third-party content object interacted with by the first user;

calculate a rank for each of the identified candidate keyword phrases based at least in part on a social-interaction history of the first user; and

send, to the client system of the first user for display in response to the first user inputting the one or more n-grams of the text query, one or more suggested queries, wherein at least one of the suggested queries comprises one of the identified candidate keyword phrases associated with a third-party content object having a rank higher than a threshold rank.

20. A system comprising:

one or more processors; and

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

receive, from a client system of a first user of an online social network, a text query comprising one or more n-grams inputted by the first user;

identify a first set of candidate keyword phrases matching the one or more n-grams of the text query, wherein each candidate keyword phrase in the first set comprises one or more n-grams extracted from content associated with a third-party content object interacted with by the first user;

calculate a rank for each of the identified candidate keyword phrases based at least in part on a social-interaction history of the first user; and

send, to the client system of the first user for display in response to the first user inputting the one or more n-grams of the text query, one or more suggested queries, wherein at least one of the suggested queries comprises one of the identified candidate keyword phrases associated with a third-party content object having a rank higher than a threshold rank.