Context sensitive ranking

• US 8,818,977 B1
• Filed: 07/12/2012
• Issued: 08/26/2014
• Est. Priority Date: 04/02/2008
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Garg, Ashutosh, Dhamdhere, Kedar
• Primary Examiner(s): Bhatia, Ajay
• Assistant Examiner(s): Le, Jessica N
• Application Number:
  US13/547,872
• Time in Patent Office: 775 Days
• US Class Current: 707/705
• CPC Class Codes: G06F 16/10 File systems; File servers
• CPC Class Codes: G06F 16/332 Query formulation
• CPC Class Codes: G06F 16/3325 Reformulation based on results of preceding query
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/3349 Reuse of stored results of previous queries
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Methods, systems, and apparatus, including computer program products, in which context can be used to rank search results. Context associated with a user session can be identified. A search query received during the user session can be used to identify a contextual click model based upon the context associated with the user session.

Claims

1. A computer-implemented method, comprising:

determining, for a current user session and by one or more computers, a user context that is indicative of a category of user interest for the user session, the determination being based on content provided to a particular user during the current user session, wherein determining the user context comprises;

determining that the content provided to the particular user during the current user session includes a hub page, the hub page being a page that has been identified as part of both a first context and a second context, the first context being different from the second context, and each of the first context and the second context being identified based at least in part on characteristics of the hub page;

identifying previous user sessions that include requests for the hub page;

obtaining a first metric for the first context, the first metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the first context;

obtaining a second metric for the second context, the second metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the second context; and

determining that the user context is one of the first context or second context based on the first metric and the second metric; and

receiving, during the user session and after determining the user context, a user search query from the particular user; and

providing, by one or more computers and in response to receiving the user search query, ranked search results for the user search query, the ranking being based at least in part on a first contextual click model for the user context.

2. The computer-implemented method of claim 1, further comprising identifying the first contextual click model, the identification comprising:

identifying previous search sessions that include selection of a web page that is identified as part of the user context;

identifying occurrences of the user search query in the identified previous search sessions;

identifying previously selected web pages that were selected in previous sessions as a result of the user search query; and

wherein the first contextual click model comprises the previously selected web pages and a selection metric defining a rate at which the previously selected web pages were selected as a result of the user search query.

3. The computer-implemented method of claim 2, wherein the first contextual click model further comprises a click quality metric for the selections of each web page.

4. The computer-implemented method of claim 1, further comprising:

determining, during the user session and by one or more computers, a second user context for the user session by comparing web pages provided to the user during the user session with a plurality of web pages defining the second user context;

identifying, by one or more computers, a second contextual click model for the user search query based upon content provided to past users during sessions that were previously identified as being associated with the second user context; and

merging the first contextual click model and the second contextual click model.

5. The computer-implemented method of claim 4, wherein merging the first contextual click model and the second contextual click model comprises scaling a selection metric for the first contextual click model based upon a probability that the first contextual click model applies and scaling a selection metric for the second contextual click model based upon a probability that the second contextual click model applies.

6. The computer-implemented method of claim 1, further comprising generating a plurality of contextual click models for respective search queries associated with the user context, each contextual click model being operable to provide weightings for search results for respective search queries, and wherein the first contextual click model is one of the plurality of contextual click models.

7. The computer-implemented method of claim 1, wherein the user context comprises one or more of a news context, a shopping context, a travel context or an educational context, wherein the news context is determined when it is identified that the user is navigating sites related to news content, the shopping context is determined when it is identified that the user is navigating sites related to shopping content, the travel context is determined when it is identified that the user is navigating travel content and the educational context is determined when it is identified that the user is navigating educational content;

and

wherein the first contextual click model associated with the user context is operable to increase the ranking of content related to the user context.

8. The computer-implemented method of claim 1, further comprising:

identifying search results responsive to the user search query; and

ranking the search results according to the first contextual click model.

9. The computer-implemented method of claim 1, wherein the characteristics of the hub page comprise an explicit context identifier provided by one or more of:

a hub page publisher; and

a context sensitive ranking system administrator.

10. The computer-implemented method of claim 1, wherein each user session is defined by a predetermined maximum period of time between the user search query and a second search query.

11. The computer-implemented method of claim 1, wherein each user session is defined by a login and logout time associated with the user.

12. The computer-implemented method of claim 1, wherein determining the user context further comprises:

providing data that causes a prompt to be provided to the particular user, the prompt displaying a choice of the first context or second context;

receiving user input indicating a selection of one of the first context or second context; and

designating the user context as the selected context.

13. A system, comprising:

a data processing apparatus; and

a data store storing instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations comprising;

determining, for a current user session and by one or more computers, a user context that is indicative of a category of user interest for the user session, the determination being based on content provided to a particular user during the current user session, wherein determining the user context comprises;

determining that the content provided to the particular user during the current user session includes a hub page, the hub page being a page that has been identified as part of both a first context and a second context, the first context being different from the second context, and each of the first context and the second context being identified based at least in part on characteristics of the hub page;

identifying previous user sessions that include requests for the hub page;

obtaining a first metric for the first context, the first metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the first context;

obtaining a second metric for the second context, the second metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the second context; and

determining that the user context is one of the first context or second context based on the first metric and the second metric; and

receiving, during the user session and after determining the user context, a user search query from the particular user; and

providing, by one or more computers and in response to receiving the user search query, ranked search results for the user search query, the ranking being based at least in part on a first contextual click model for the user context.

14. The system of claim 13, wherein the operations further comprise identifying the first contextual click model, the identification comprising:

identifying previous search sessions that include selection of a web page that is identified as part of the user context;

identifying occurrences of the user search query in the identified previous search sessions;

identifying previously selected web pages that were selected in previous sessions as a result of the user search query; and

wherein the first contextual click model comprises the previously selected web pages and a selection metric defining a rate at which the previously selected web pages were selected as a result of the user search query.

15. The system of claim 14, wherein the first contextual click model further comprises a click quality metric for the selections of each web page.

16. The system of claim 13, wherein the operations further comprise:

determining, during the user session and by one or more computers, a second user context for the user session by comparing web pages provided to the user during the user session with a plurality of web pages defining the second user context;

identifying, by one or more computers, a second contextual click model for the user search query based upon content provided to past users during sessions that were previously identified as being associated with the second user context; and

merging the first contextual click model and the second contextual click model.

17. The system of claim 16, wherein merging the first contextual click model and the second contextual click model comprises scaling a selection metric for the first contextual click model based upon a probability that the first contextual click model applies and scaling a selection metric for the second contextual click model based upon a probability that the second contextual click model applies.

18. The system of claim 13, wherein the operations further comprise generating a plurality of contextual click models for respective search queries associated with the user context, each contextual click model being operable to provide weightings for search results for respective search queries, and wherein the first contextual click model is one of the plurality of contextual click models.

19. The system of claim 13, wherein the user context comprises one or more of a news context, a shopping context, a travel context or an educational context, wherein the news context is determined when it is identified that the user is navigating sites related to news content, the shopping context is determined when it is identified that the user is navigating sites related to shopping content, the travel context is determined when it is identified that the user is navigating travel content and the educational context is determined when it is identified that the user is navigating educational content;

and

wherein the first contextual click model associated with the user context is operable to increase the ranking of content related to the user context.

20. The system of claim 13, wherein the operations further comprise:

identifying search results responsive to the user search query; and

ranking the search results according to the first contextual click model.

21. The system of claim 13, wherein the characteristics of the hub page comprise an explicit context identifier provided by one or more of:

a hub page publisher; and

a context sensitive ranking system administrator.

22. The system of claim 13, wherein determining the user context further comprises:

providing data that causes a prompt to be provided to the particular user, the prompt displaying a choice of the first context or second context;

receiving user input indicating a selection of one of the first context or second context; and

designating the user context as the selected context.

23. One or more non-transitory computer readable media, comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising:

determining, for a current user session and by one or more computers, a user context that is indicative of a category of user interest for the user session, the determination being based on content provided to a particular user during the current user session, wherein determining the user context comprises;

determining that the content provided to the particular user during the current user session includes a hub page, the hub page being a page that has been identified as part of both a first context and a second context, the first context being different from the second context, and each of the first context and the second context being identified based at least in part on characteristics of the hub page;

identifying previous user sessions that include requests for the hub page;

obtaining a first metric for the first context, the first metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the first context;

obtaining a second metric for the second context, the second metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the second context; and

determining that the user context is one of the first context or second context based on the first metric and the second metric; and

receiving, during the user session and after determining the user context, a user search query from the particular user; and

providing, by one or more computers and in response to receiving the user search query, ranked search results for the user search query, the ranking being based at least in part on a first contextual click model for the user context.

24. A computer-implemented method, comprising:

determining, for a current user session and by one or more computers, a user context that is indicative of a category of user interest for the user session, the determination being based on content provided to a particular user during the current user session, wherein determining the user context comprises;

identifying a plurality of web pages as part of one or more contexts, each of the web pages being identified based upon inclusion of the web pages in one or more of a plurality of click models associated with the one or more contexts;

determining that the content provided to the particular user during the current user session includes a particular web page from the plurality of web pages, the particular web page being identified as part of both a first context and a second context, the first context being different from the second context;

obtaining a first metric for the first context, the first metric being calculated based on a frequency with which previous user sessions that included requests for the particular web page were determined to be part of the first context;

obtaining a second metric for the second context, the second metric being calculated based on a frequency with which the previous user sessions that included requests for the particular web page were determined to be part of the second context; and

determining that the user context is one of the first context or second context based on the first metric and the second metric; and

receiving, during the user session and after determining the user context, a user search query from the particular user; and

providing, by one or more computers and in response to receiving the user search query, ranked search results for the user search query, the ranking being based at least in part on a first contextual click model for the user context.

25. The method of claim 24, wherein each user session is defined by a predetermined maximum period of time between the user search query and a second search query.

26. The method of claim 24, wherein each user session is defined by a login and logout time associated with a corresponding user.

27. The method of claim 24, further comprising:

generating a plurality of contextual click models for respective search queries associated with the user context, each contextual click model being operable to provide weightings of search results for respective search queries.

28. The method of claim 24, further comprising:

determining that a second search query was not received within a predetermined period of time of the user search query; and

bypassing the provision of the first contextual click model.

29. The method of claim 24, wherein determining the user context further comprises:

providing data that causes a prompt to be provided to the particular user, the prompt displaying a choice of the first context or second context;

receiving user input indicating a selection of one of the first context or second context; and

designating the user context as the selected context.

30. A system, comprising:

a data processing apparatus; and

a data store storing instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations comprising;

determining, for a current user session and by one or more computers, a user context that is indicative of a category of user interest for the user session, the determination being based on content provided to a particular user during the current user session, wherein determining the user context comprises;

identifying a plurality of web pages as part of one or more contexts, each of the web pages being identified based upon inclusion of the web pages in one or more of a plurality of click models associated with the one or more contexts;

determining that the content provided to the particular user during the current user session includes a particular web page from the plurality of web pages, the particular web page being identified as part of both a first context and a second context, the first context being different from the second context;

obtaining a first metric for the first context, the first metric being calculated based on a frequency with which previous user sessions that included requests for the particular web page were determined to be part of the first context;

obtaining a second metric for the second context, the second metric being calculated based on a frequency with which the previous user sessions that included requests for the particular web page were determined to be part of the second context; and

determining that the user context is one of the first context or second context based on the first metric and the second metric; and

receiving, during the user session and after determining the user context, a user search query from the particular user; and

providing, by one or more computers and in response to receiving the user search query, ranked search results for the user search query, the ranking being based at least in part on a first contextual click model for the user context.

31. The system of claim 30, wherein each user session is defined by a predetermined maximum period of time between the user search query and a second search query.

32. The system of claim 30, wherein each user session is defined by a login and logout time associated with a corresponding user.

33. The system of claim 30, the operations further comprising:

generating a plurality of contextual click models for respective search queries associated with the user context, each contextual click model being operable to provide weightings of search results for respective search queries.

34. The system of claim 30, the operations further comprising:

determining that a second search query was not received within a predetermined period of time of the user search query; and

bypassing the provision of the first contextual click model.

35. The system of claim 30, wherein determining the user context further comprises:

providing data that causes a prompt to be provided to the particular user, the prompt displaying a choice of the first context or second context;

receiving user input indicating a selection of one of the first context or second context; and

designating the user context as the selected context.

36. One or more non-transitory computer readable media, comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising:

determining, for a current user session and by one or more computers, a user context that is indicative of a category of user interest for the user session, the determination being based on content provided to a particular user during the current user session, wherein determining the user context comprises;

identifying a plurality of web pages as part of one or more contexts, each of the web pages being identified based upon inclusion of the web pages in one or more of a plurality of click models associated with the one or more contexts;

determining that the content provided to the particular user during the current user session includes a particular web page from the plurality of web pages, the particular web page being identified as part of both a first context and a second context, the first context being different from the second context;

obtaining a first metric for the first context, the first metric being calculated based on a frequency with which previous user sessions that included requests for the particular web page were determined to be part of the first context;

obtaining a second metric for the second context, the second metric being calculated based on a frequency with which the previous user sessions that included requests for the particular web page were determined to be part of the second context; and

determining that the user context is one of the first context or second context based on the first metric and the second metric; and

receiving, during the user session and after determining the user context, a user search query from the particular user; and

providing, by one or more computers and in response to receiving the user search query, ranked search results for the user search query, the ranking being based at least in part on a first contextual click model for the user context.