Systems and methods for promoting personalized search results based on personal information

• US 8,620,915 B1
• Filed: 08/28/2007
• Issued: 12/31/2013
• Est. Priority Date: 03/13/2007
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Brukman, Michael Y., Horling, Bryan C., Zamir, Oren E.
• Primary Examiner(s): BURKE, JEFF A
• Application Number:
  US11/846,346
• Time in Patent Office: 2,317 Days
• Field of Search: 707/3, 707/722, 707/736, 707/723, 707/727, 707/732
• US Class Current: 707/733
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

An information server identifies a list of search results associated with a search query from a user. Each search result has an initial position in the list. The information server then identifies a user profile associated with the user. In some embodiments, the user profile includes a set of user-preferred search results that is determined at least in part from the user'"'"'s search history and one or more supplemental search results that are deemed similar to at least one of the user-preferred search results. Among the list of search results, the information server identifies one or more search results that are associated with the supplemental search results. Finally, the information server reorders the list of search results by moving each of the identified search results from its initial position by an offset and then provides the reordered list of search results to the user.

Claims

1. A computer-implemented method performed at a server system having one or more processors and memory, the method comprising:

receiving a search query from a user;

identifying a list of search results associated with the search query, wherein each search result has an initial position in the list;

identifying a user profile associated with the user, wherein the user profile includes a set of user-preferred search results that is determined, at least in part, by;

identifying a set of candidate search results in a search history of the user, wherein each of the candidate search results has been selected by the user for at least a predefined minimum number of times;

determining a popularity metric for each of the candidate search results; and

selecting a subset of the candidate search results whose associated popularity metrics exceed a predefined threshold as the set of user-preferred search results;

identifying in the list of search results, one or more search results that are associated with at least one of the user-preferred search results;

reordering the list of search results by moving each of the identified search results from its initial position by an offset; and

providing the reordered list of search results to the user.

2. The method of claim 1, wherein the offset is a constant for the identified search results.

3. The method of claim 1, wherein the offset is a variable that is a function of a popularity metric associated with each of the identified search results.

4. The method of claim 1, wherein each of the candidate search results has been selected by the user for at least the predefined minimum number of times during a time period having at least a predefined minimum duration.

5. The method of claim 1, wherein the popularity metric of a candidate search result is a function of one or more parameters including at least one parameter which is a time span period from the user'"'"'s most remote selection of the candidate search result to the user'"'"'s most recent selection of the candidate search result.

6. The method of claim 1, wherein the popularity metric of a candidate search result is determined by:

determining a model predicting a likelihood of a user selection of a search result using multiple users'"'"' search histories; and

applying the model to the user'"'"'s search history to generate the popularity metric of the candidate search result.

7. The method of claim 6, wherein the model is determined by:

generating a profile that characterizes the multiple users'"'"' behaviors over multiple search results in the multiple users'"'"' search histories; and

determining as the model a set of coefficients from the profile, wherein the set of coefficients, when applied to a set of search results associated from a specific user'"'"'s search history, is configured to predict the likelihood of the specific user'"'"'s selection of each of the search results in the future.

8. The method of claim 1, wherein the predefined minimum number of times is at least two.

9. The method of claim 1, wherein the popularity metric of a candidate search result is a function of two or more parameters, wherein the parameters include:

a time span period from the user'"'"'s most remote selection of the candidate search result to the user'"'"'s most recent selection of the candidate search result; and

a time decay period from the user'"'"'s most recent selection of the candidate search result to present.

10. A computer system for producing personalized search results, comprising:

memory;

one or more processors;

one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including;

instructions for receiving a search query from a user;

instructions for identifying a list of search results associated with the search query, wherein each search result has an initial position in the list;

instructions for identifying a user profile associated with the user, wherein the user profile includes a set of user-preferred search results determined, at least in part, by;

instructions for identifying a set of candidate search results in a search history of the user, wherein each of the candidate search results has been selected by the user for at least a predefined minimum number of times;

instructions for determining a popularity metric for each of the candidate search results; and

instructions for selecting a subset of the candidate search results whose associated popularity metrics exceed a predefined threshold as the set of user-preferred search results;

instructions for identifying in the list of search results, one or more search results that are associated with at least one of the user-preferred search results;

instructions for reordering the list of search results by moving each of the identified search results from its initial position by an offset; and

instructions for providing the reordered list of search results to the user.

11. The computer system of claim 10, wherein the offset is a constant for the identified search results.

12. The computer system of claim 10, wherein the offset is a variable that is a function of a popularity metric associated with each of the identified search results.

13. The computer system of claim 10, wherein each of the candidate search results has been selected by the user for at least the predefined minimum number of times during a time period having at least a predefined minimum duration.

14. The computer system of claim 10, wherein the popularity metric of a candidate search result is a function of one or more parameters including at least one parameter which is a time span period from the user'"'"'s most remote selection of the candidate search result to the user'"'"'s most recent selection of the candidate search result.

15. The computer system of claim 10, wherein the instructions for determining the popularity metric of a candidate search result further include:

instructions for determining a model predicting a likelihood of a user selection of a search result using multiple users'"'"' search histories; and

instructions for applying the model to the user'"'"'s search history to generate the popularity metric of the candidate search result.

16. The computer system of claim 15, wherein the instructions for determining the model further include:

instructions for generating a profile that characterizes the multiple users'"'"' behaviors over multiple search results in the multiple users'"'"' search histories; and

instructions for determining as the model a set of coefficients from the profile, wherein the set of coefficients, when applied to a set of search results associated from a specific user'"'"'s search history, is configured to predict the likelihood of the specific user'"'"'s selection of each of the search results in the future.

17. The computer system of claim 10, wherein the predefined number of times is at least two.

18. The computer system of claim 10, wherein the popularity metric of a candidate search result is a function of two or more parameters, wherein the parameters include:

a time span period from the user'"'"'s most remote selection of the candidate search result to the user'"'"'s most recent selection of the candidate search result; and

a time decay period from the user'"'"'s most recent selection of the candidate search result to present.

19. A non-transitory computer readable storage medium storing one or more programs to be executed by a computer system, the one or more programs comprising:

instructions for receiving a search query from a user;

instructions for identifying a list of search results associated with the search query, wherein each search result has an initial position in the list;

instructions for identifying a user profile associated with the user, wherein the user profile includes a set of user-preferred search results determined, at least in part, by;

instructions for identifying a set of candidate search results in a search history of the user, wherein each of the candidate search results has been selected by the user for at least a predefined minimum number of times;

instructions for determining a popularity metric for each of the candidate search results; and

instructions for selecting a subset of the candidate search results whose associated popularity metrics exceed a predefined threshold as the set of user-preferred search results;

instructions for identifying in the list of search results, one or more search results that are associated with at least one of the user-preferred search results;

instructions for reordering the list of search results by moving each of the identified search results from its initial position by an offset; and

instructions for providing the reordered list of search results to the user.

20. The non-transitory computer readable storage medium of claim 19, wherein each of the candidate search results has been selected by the user for at least the predefined minimum number of times during a time period having at least a predefined minimum duration.

21. The non-transitory computer readable storage medium of claim 19, wherein the popularity metric of a candidate search result is a function of one or more parameters including at least one parameter which is a time span period from the user'"'"'s most remote selection of the candidate search result to the user'"'"'s most recent selection of the candidate search result, and a time decay period from the user'"'"'s most recent selection of the candidate search result to present.

22. The non-transitory computer readable storage medium of claim 19, wherein the instructions for determining the popularity metric of a candidate search result further include:

instructions for determining a model predicting a likelihood of a user selection of a search result using multiple users'"'"' search histories; and

instructions for applying the model to the user'"'"'s search history to generate the popularity metric of the candidate search result.

23. The non-transitory computer readable storage medium of claim 22, wherein the instructions for determining the model further include:

instructions for generating a profile that characterizes the multiple users'"'"' behaviors over multiple search results in the multiple users'"'"' search histories; and

instructions for determining as the model a set of coefficients from the profile, wherein the set of coefficients, when applied to a set of search results associated from a specific user'"'"'s search history, is configured to predict the likelihood of the specific user'"'"'s selection of each of the search results in the future.

24. The non-transitory computer readable storage medium of claim 19, wherein the predefined minimum number of times is at least two.

25. The non-transitory computer readable storage medium of claim 19, wherein the popularity metric of a candidate search result is a function of two or more parameters, wherein the parameters include:

a time span period from the user'"'"'s most remote selection of the candidate search result to the user'"'"'s most recent selection of the candidate search result; and

a time decay period from the user'"'"'s most recent selection of the candidate search result to present.

26. A computer system for producing personalized search results comprising:

memory;

one or more processors;

one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including;

means for receiving a search query from a user;

means for identifying a list of search results associated with the search query, wherein each search result has an initial position in the list;

means for identifying a user profile associated with the user, wherein the user profile includes a set of user-preferred search results determined, at least in part, by;

means for identifying a set of candidate search results in a search history of the user, wherein each of the candidate search results has been selected by the user for at least a predefined minimum number of times;

means for determining a popularity metric for each of the candidate search results; and

means for selecting a subset of the candidate search results whose associated popularity metrics exceed a predefined threshold as the set of user-preferred search results;

means for identifying in the list of search results, one or more search results that are associated with at least one of the user-preferred search results;

means for reordering the list of search results by moving each of the identified search results from its initial position by an offset; and

means for providing the reordered list of search results to the user.