Social search engine

• US 8,935,192 B1
• Filed: 09/14/2012
• Issued: 01/13/2015
• Est. Priority Date: 04/22/2010
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Ventilla, Max, Spiro, Robert J., Horowitz, Damon, Kamvar, Sepandar D., Davies, Winton Henry Ellis, Kim, Michael, MacCartney, William C.
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): SITIRICHE, LUIS A
• Application Number:
  US13/620,354
• Time in Patent Office: 851 Days
• Field of Search: None
• US Class Current: 706/46
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Methods, systems, and apparatus, including computer programs, for a social search engine. In one aspect, a method includes receiving an answer to a question from an answerer; identifying an entity in the answer, in which the entity refers to a product, a service, a company, or a merchant; obtaining permission from the answerer to provide an interactive link for the entity in the answer to a resource from which the entity can be obtained; receiving a user submitted query and determining that the query is related to the entity; and responsive to determining, providing the answer, including the link to the resource, as part of search results that are responsive to the query.

Claims

1. A method implemented by data processing apparatus, the method comprising:

determining, by the data processing apparatus, and for each of one or more topics associated with the user, an expertise score indicating a level of expertise the user has with the respective topic, a responsiveness score indicating responsiveness of the user to previous questions routed to the user about the respective topic, and a quality score indicating a quality of answers, by the user, to the previous questions about the respective topic;

obtaining, by the data processing apparatus, one or more additional topics, not explicitly associated with the user;

determining, by the data processing apparatus, and for each of one or more additional topics, an expertise score indicating a level of expertise the user has with the respective additional topic;

associating, by the data processing apparatus, and for each of the one or more topics and the one or more additional topics, the expertise score, the responsiveness score, and the quality score with the user; and

selectively routing, b the data one or more queries to the user in response to the user being identified as a candidate answerer from a plurality of users based on one or more of the expertise score, the responsiveness score and the quality score.

2. The method of claim 1, where the plurality of sources includes one or more of the following:

topics explicitly provided by the user, topics explicitly provided by members of the social affinity group, topics parsed from one or more profile pages of the user, topics extracted from unstructured text associated with the user, topics extracted from images or videos associated with the user, topics extracted from status message updates of the user, or topics parsed from resources linked to by the user.

3. The method of claim 1, further comprising associating one or more of the topics with each member in the social affinity group.

4. The method of claim 1, further comprising:

receiving a question from an asker;

determining one or more appropriate topics for the question; and

selecting an answerer based on a respective expertise score, a respective responsiveness score, and a respective quality score.

5. The method of claim 1, further comprising:

receiving a question from an asker;

receiving an answer for the question from an answerer;

identifying an entity in the answer;

determining an interactive link to a resource, of an affiliate, from which the entity can be obtained; and

providing to the asker the interactive link with the answer.

6. The method of claim 5, wherein identifying the entity in the answer further comprises using a trained classifier to determine if the answer refers to an entity.

7. The method of claim 1, where the responsive score is based on the user explicitly muting the respective topic or declining to answer questions about the respective topic when given an opportunity.

8. The method of claim 1, where the quality score is based on the user receiving negative feedback from another user on an answer about the respective topic.

9. The method of claim 1, where the expertise score is based on the plurality of topic sources.

10. A system comprising:

a processor; and

computer-readable medium coupled to the processor and having instructions stored thereon, which, when executed by the processor, cause the processor to perform operations comprising;

determining, for each of one or more topics associated with the user, an expertise score indicating a level of expertise the user has with the respective topic, a responsiveness score indicating responsiveness of the user to previous questions routed to the user about the respective topic, and a quality score indicating a quality of answers, by the user, to the previous questions about the respective topic;

obtaining one or more additional topics, not explicitly associated with the user;

determining, for each of one or more additional topics, an expertise score indicating a level of expertise the user has with the respective additional topic;

associating for each of the one or more topics and the one or more additional topics, the expertise score, the responsiveness score, and the quality score with the user; and

selectively routing one or more queries to the user in response to the user being identified as a candidate answerer from a plurality of users based on one or more of the expertise score, the responsiveness score and the quality score.

11. The system of claim 10, where the plurality of sources includes one or more of the following:

topics explicitly provided by the user, topics explicitly provided by members of the social affinity group, topics parsed from one or more profile pages of the user, topics extracted from unstructured text associated with the user, topics extracted from images or videos associated with the user, topics extracted from status message updates of the user, or topics parsed from resources linked to by the user.

12. The system of claim 10, further comprising associating one or more of the topics with each member in the social affinity group.

13. The system of claim 10, further comprising:

receiving a question from an asker;

determining one or more appropriate topics for the question; and

selecting an answerer based on a respective expertise score, a respective responsiveness score, and a respective quality score.

14. The system of claim 10, further comprising:

receiving a question from an asker;

receiving an answer for the question from an answerer;

identifying an entity in the answer;

determining an interactive link to a resource, of an affiliate, from which the entity can be obtained; and

providing to the asker the interactive link with the answer.

15. The system of claim 14, wherein identifying the entity in the answer further comprises using a trained classifier to determine if the answer refers to an entity.

16. The system of claim 10, where the responsive score is based on the user explicitly muting the respective topic or declining to answer questions about the respective topic when given an opportunity.

17. The system of claim 10, where the quality score is based on the user receiving negative feedback from another user on an answer about the respective topic.

18. The system of claim 10, where the expertise score is based on the plurality of topic sources.

19. A non-transitory computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the processor to perform operations comprising:

determining, for each of one or more topics associated with the user, an expertise score indicating a level of expertise the user has with the respective topic, a responsiveness score indicating responsiveness of the user to previous questions routed to the user about the respective topic, and a quality score indicating a quality of answers, by the user, to the previous questions about the respective topic;

obtaining one or more additional topics, not explicitly associated with the user;

determining, for each of one or more additional topics, an expertise score indicating a level of expertise the user has with the respective additional topic;

associating for each of the one or more topics and the one or more additional topics, the expertise score, the responsiveness score, and the quality score with the user; and

selectively routing one or more queries to the user in response to the user being identified as a candidate answerer from a plurality of users based on one or more of the expertise score, the responsiveness score and the quality score.

20. The computer-readable medium of claim 19, where the plurality of sources includes one or more of the following:

topics explicitly provided by the user, topics explicitly provided by members of the social affinity group, topics parsed from one or more profile pages of the user, topics extracted from unstructured text associated with the user, topics extracted from images or videos associated with the user, topics extracted from status message updates of the user, or topics parsed from resources linked to by the user.

21. The computer-readable medium of claim 19, further comprising associating one or more of the topics with each member in the social affinity group.

22. The computer-readable medium of claim 19, further comprising:

receiving a question from an asker;

determining one or more appropriate topics for the question; and

selecting an answerer based on a respective expertise score, a respective responsiveness score, and a respective quality score.

23. The computer-readable medium of claim 19, further comprising:

receiving a question from an asker;

receiving an answer for the question from an answerer;

identifying an entity in the answer;

determining an interactive link to a resource, of an affiliate, from which the entity can be obtained; and

providing to the asker the interactive link with the answer.

24. The computer-readable medium of claim 23, wherein identifying the entity in the answer further comprises using a trained classifier to determine if the answer refers to an entity.

25. The computer-readable medium of claim 19, where the responsive score is based on the user explicitly muting the respective topic or declining to answer questions about the respective topic when given an opportunity.

26. The computer-readable medium of claim 19, where the quality score is based on the user receiving negative feedback from another user on an answer about the respective topic.

27. The computer-readable medium of claim 19, where the expertise score is based on the plurality of topic sources.