IDENTIFYING AND PROCESSING RECOMMENDATION REQUESTS

• US 20160042069A1
• Filed: 08/08/2014
• Published: 02/11/2016
• Est. Priority Date: 08/08/2014
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Lee-Goldman, Russell, Kesten, Yuval, Campbell, Amy, Fiore, Andrew Rocco Tresolini, Adamic, Lada Ariana, Goldblatt, David M., Rich, Mark Andrew, Gupta, Nidhi
• Granted Patent: US 10,127,316 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24522 Translation of natural language queries to structured queries
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 40/295 Named entity recognition
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0257 User requested
• CPC Class Codes: G06Q 50/00 Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism healthcare informatics G16H
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

In one embodiment, a method includes receiving unstructured text from a user of a social-networking system, determining whether the unstructured text includes a request for a recommendation, identifying one or more first entity names in the unstructured text, generating a structured query based upon the one or more first entity names, identifying, in the social graph, one or more second entity names corresponding to the structured query, and presenting the one or more second entity names and the unstructured text in a social context of the user. The unstructured text may include text of a post or message generated by the user on a social-networking system. A score may be generated based on the unstructured text to determine whether the text includes a request for recommendation using a machine-learning model based on comparison of the unstructured text to the one or more predetermined words associated with requests for recommendation.

Claims

1. A method comprising:

by a computing device, receiving unstructured text from a user of a social-networking system;

by the computing device, determining whether the unstructured text comprises a request for a recommendation;

by the computing device, identifying one or more first entity names in the unstructured text;

by the computing device, generating a structured query based upon the one or more first entity names;

by the computing device, identifying, in the social graph, one or more second entity names corresponding to the structured query; and

by the computing device, presenting the one or more second entity names and the unstructured text in a social context of the user.

2. The method of claim 1, wherein the unstructured text comprises text of a post or message generated by the user on a social-networking system.

3. The method of claim 1, wherein determining whether the unstructured text comprises a request for a recommendation comprises determining whether the unstructured text matches one or more predetermined words associated with requests for recommendation.

4. The method of claim 3, wherein determining whether the unstructured text comprises a request for a recommendation comprises:

generating a score based on the unstructured text using a machine-learning model based on comparison of the unstructured text to the one or more predetermined words associated with requests for recommendation, wherein the unstructured text comprises a request for a recommendation when the score is greater than a threshold value.

5. The method of claim 1, wherein the one or more second entity names correspond to one or more concept nodes in the social graph.

6. The method of claim 5, wherein the one or more second entity names correspond to one or more places represented by the one or more concept nodes.

7. The method of claim 1, wherein identifying the one or more first entity names in the unstructured text comprises using a machine-learning topic tagger model to identify words or phrases in the unstructured text that correspond to names of entities in the social graph.

8. The method of claim 1, wherein generating the structured query comprises:

combining the one or more first entity names to form a first query;

submitting the first query to a structured-query generator configured to generate the structured query based on the one or more first entity names; and

receiving one or more results from the structured-query generator, wherein the structured query is based upon the one or more results.

9. The method of claim 8, wherein identifying the one or more second entity names corresponding to the structured query comprises:

submitting the structured query to a graph search engine configured to search the social graph for entities that match the structured query;

receiving results from the graph search engine, wherein the one or more second entity names are based on received results.

10. The method of claim 1, wherein generating the structured query comprises:

determining whether the one or more first entity names comprise a category name and a city name; and

when the one or more first entity names comprise a category name and a city name, generating the structured query based on the category name and the city name, wherein the structured query comprises a first constraint that selects an entity having the category name and the city name.

11. The method of claim 1, further comprising:

by the computing device, identifying one or more authors of one or more reviews of one or more reviewed entities;

by the computing device, generating one or more scores for the one or more authors based on relevance of the respective one or more reviewed entities to the structured query; and

by the computing device, presenting one or more names of each of the one or more authors having a score greater than a threshold value, wherein the presenting is in a social context of the user.

12. The method of claim 11, wherein identifying one or more authors of one or more reviews of one or more reviewed entities comprises identifying, in the social graph, one or more user nodes based on a query for users who have reviewed the one or more entities in the social graph, wherein the one or more reviewed entities are identified based on the structured query.

13. The method of claim 11, wherein the one or more reviewed entities are selected from the one or more second entities.

14. The method of claim 11, further comprising:

by the computing device, presenting at least a portion of the one or more reviews in association with the one or more names of the one or more authors of the respective one or more reviews, wherein the presenting is in the social context of the user.

15. The method of claim 11, wherein the one or more authors comprise users of the social-networking system.

16. The method of claim 1, wherein presenting the one or more second entity names and the unstructured text in a social context of the user comprises displaying the unstructured text and the one or more second entity names in association with a news feed story associated with the user.

17. The method of claim 16, further comprising:

receiving one or more comments from one or more users; and

displaying the one or more comments in association with the news feed story.

18. The method of claim 1, wherein presenting the one or more second entity names comprises displaying the one or more second entity names to the user in a notification user interface in association with the unstructured query.

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

receive unstructured text from a user of a social-networking system;

determine whether the unstructured text comprises a request for a recommendation;

identify one or more first entity names in the unstructured text;

generate a structured query based upon the one or more first entity names;

identify, in the social graph, one or more second entity names corresponding to the structured query; and

present the one or more second entity names and the unstructured text in a social context of the user.

20. 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;

receive unstructured text from a user of a social-networking system;

determine whether the unstructured text comprises a request for a recommendation;

identify one or more first entity names in the unstructured text;

generate a structured query based upon the one or more first entity names;

identify, in the social graph, one or more second entity names corresponding to the structured query; and

present the one or more second entity names and the unstructured text in a social context of the user.