SYSTEM, METHOD, OR APPARATUS RELATING TO CATEGORIZING OR SELECTING POTENTIAL SEARCH RESULTS

• US 20100287129A1
• Filed: 05/07/2009
• Published: 11/11/2010
• Est. Priority Date: 05/07/2009
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Suchter, Sean, Bhattacharjee, Arnab, Tomkins, Andrew, Pavlovski, Dmitri, Chan, Su Han, Tsioutsiouliklis, Kostas
• Application Number:
  US12/437,043
• Publication Number:
  US 20100287129A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Embodiments of methods, apparatuses, devices and systems associated with categorizing or selecting potential search engine results are disclosed.

Claims

1. A method comprising:

receiving via a network communication adaptor of a special purpose computing apparatus one or more signals representing a user behavior log;

executing one or more instruction on said special purpose computing apparatus to form one or more signals representing a training data set associated with one or more documents based at least in part on one or more portions of information derived from said user behavior log;

determine a correlation between the one or more documents and a prior response;

calculate a prediction score for one or more additional documents based, at least in part, on said determined correlation; and

with said special purpose computing apparatus, store a signal representative of an association of one or more additional documents with one or more categories of documents in a memory device based at least in part on the prediction scores calculated for said one or more additional documents.

2. The method of claim 1, wherein said prior response comprises a likelihood that a particular document will be displayed to a user

3. The method of claim 1, wherein said prior response comprises a likelihood that a particular document will be selected by a user.

4. The method of claim 1, and further comprising executing one or more additional instructions on said special purpose computing apparatus to determine a correlation between said one or more documents and a prior response at least in part by analyzing one or more aspects of one or more feature vectors associated with said one or more documents along with said user behavior log.

5. The method of claim 1, and further comprising executing one or more additional instructions on said special purpose computing apparatus to calculate a prediction score for one or more additional documents at least in part by comparing one or more aspects of one or more feature vectors associated with said one or more documents to one or more aspects of one or more additional features vectors associated with said one or more additional documents along with said determined correlation.

6. The method of claim 1, wherein said one or more categories of documents comprise one or more tiers of documents.

7. The method of claim 6, wherein said one or more tiers of documents comprise one or more memory locations for storing information associated with documents.

8. The method of claim 7, wherein said assigning comprises assigning one of the one or more additional documents having a prediction score above a threshold value to a first tier of documents and assigning another one of the one or more additional documents having a prediction score below a threshold value to a second tier of documents.

9. An article comprising:

a storage medium have instructions stored thereon, wherein said instructions, if executed by a special purpose computing apparatus, enable said special purpose computing apparatus to;

read one or more signals representative of a user behavior log from a memory device associated with said special purpose computing apparatus;

form one or more signals representing a training data set associated with one or more documents based at least in part on one or more portions of information derived from said user behavior log;

determine a correlation between the one or more documents and a prior response;

calculate a prediction score for one or more additional documents based at least in part on said determined correlation; and

store a signal representative of an association of one or more additional documents with one or more categories of documents based at least in part on the prediction scores calculated for said one or more additional documents.

10. The article of claim 9, wherein said prior response comprises a likelihood that a particular document will be displayed to a user.

11. The article of claim 9, wherein said prior response comprises a likelihood that a particular document will be selected by a user.

12. The article of claim 9, wherein said one or more categories of documents comprise one or more tiers of documents, wherein said one or more tiers of documents comprise one or more memory locations for storing information associated with documents.

13. The article of claim 12, wherein said instructions, if executed by said special purpose computing apparatus, further enable said special purpose computing apparatus to store one of the one or more additional documents having a prediction score above a threshold value to a first tier of documents and store another one of the one or more additional documents having a prediction score below a threshold value to a second tier of documents.

14. The article of claim 9, wherein said user behavior log comprises one or more signals representing one or more aspects of user behavior at least in part in response to one or more search results.

15. An apparatus comprising:

a special purpose computing apparatus;

said special purpose computing apparatus comprising a network communication adaptor to receive one or more signals representing a user behavior log;

said special purpose computing apparatus further comprising one or more processors programmed with one or more instructions to;

form one or more signals representing a training data set associated with one or more documents based at least in part on one or more portions of information derived from said user behavior log;

determine a correlation between the one or more documents and a prior response;

calculate a prediction score for one or more additional documents based at least in part on said determined correlation; and

store a signal representative of an association of one or more additional documents with one or more categories of documents based at least in part on the prediction scores calculated for said one or more additional documents.

16. The apparatus of claim 15, wherein said prior response comprises a likelihood that a particular document will be displayed to a user and/or selected by a user.

17. The apparatus of claim 15, wherein said user behavior log comprises one or more signals representing one or more aspects of user behavior at least in part in response to one or more search results.

18. The apparatus of claim 15, wherein said one or more aspects of user behavior comprises user selections of a link to a particular document, user interaction with a particular document, and/or an amount of time a user spends with a particular document.

19. The apparatus of claim 15, wherein said one or more categories of documents comprise one or more tiers of documents, wherein said one or more tiers of documents comprise one or more memory locations for storing information associated with documents.

20. The apparatus of claim 19, wherein said one or more processors are further programmed with one or more additional instructions to store signals representative of one of the one or more additional documents having a prediction score above a threshold value to a first tier of documents and store signals representative of another one of the one or more additional documents having a prediction score below a threshold value to a second tier of documents.