RANKING SPECIALIZATION FOR A SEARCH

• US 20120011112A1
• Filed: 07/06/2010
• Published: 01/12/2012
• Est. Priority Date: 07/06/2010
• Status: Abandoned Application

• Current Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Li, Xin, Li, Fan, Zheng, Zhaohui, Zha, Hongyuan, Bian, Jiang
• Application Number:
  US12/831,014
• Publication Number:
  US 20120011112A1
• Time in Patent Office: Days
• US Class Current: 707/723
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/953 Querying, e.g. by the use of web search engines
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]

Abstract

Example methods, apparatuses, and articles of manufacture are disclosed that may be used to provide or otherwise support one or more ranking specialization techniques for use with search engine information management systems.

Claims

1. A method comprising:

electronically identifying a plurality of ranking-sensitive query topics; and

concurrently training a plurality of ranking functions associated with said plurality of ranking-sensitive query topics based, at least in part, on an application of a loss function, wherein at least one ranking function of said plurality of ranking functions corresponds to at least one ranking-sensitive query topic.

2. The method of claim 1, wherein said electronically identifying said plurality of ranking-sensitive query topics comprises:

electronically generating one or more query features based, at least in part, on ranking features received in response to one or more digital signals representing training queries, wherein said ranking features comprise one or more feature vectors associated with said one or more training queries; and

establishing one or more clusters representative of said plurality of ranking-sensitive query topics based, at least in part, on one or more machine-learned functions.

3. The method of claim 2, wherein said one or more machine-learned functions operates in an unsupervised mode.

4. The method of claim 3, wherein said one or more machine-learned functions operating in said unsupervised mode identifies one or more digital signals representing a vector distance of said one or more feature vectors.

5. The method of claim 4, wherein said vector distance of said one or more feature vectors is determined based, at least in part, on at least one of the following:

a Pearson correlation;

or a weighted Pearson correlation.

6. The method of claim 1, wherein said loss function comprises a global loss function determined substantially in accordance with at least one linear function.

7. The method of claim 6, wherein said at least one linear function comprises a Topical Ranking Support Vector Machine (SVM) function.

8. A method comprising:

electronically calculating, using at least one ranking function corresponding to at least one ranking-sensitive query topic, a relevance score for one or more documents received in response to digital signals representing a query based, at least in part, on a measure of correlation between said at least one ranking-sensitive query topic and said query.

9. The method of claim 8, wherein said measure of correlation comprises a statistical probability of said query belonging to said at least one ranking-sensitive query topic.

10. The method of claim 8, and further comprising:

electronically determining an adjusted ranking score for said one or more documents by aggregating said calculated relevance scores.

11. The method of claim 10, wherein said aggregating said calculated relevance scores is based, at least in part, on a weighted sum of said relevance scores.

12. The method of claim 11, wherein said weighted sum of said relevance scores is estimated based, at least in part, on a statistical probability of said query belonging to said at least one ranking-sensitive query topic.

13. An article comprising:

a storage medium having instructions stored thereon executable by a special purpose computing platform to;

electronically identify a plurality of ranking-sensitive query topics; and

concurrently train a plurality of ranking functions associated with said plurality of ranking-sensitive query topics based, at least in part, on an application of a loss function, wherein at least one ranking function of said plurality of ranking functions corresponds to at least one ranking-sensitive query topic.

14. The article of claim 13, wherein said storage medium further includes instructions to:

electronically generate one or more query features based, at least in part, on ranking features received in response to one or more digital signals representing training queries, wherein said ranking features comprise one or more feature vectors associated with said one or more training queries; and

establish one or more clusters representative of said plurality of ranking-sensitive query topics based, at least in part, on one or more machine-learned functions.

15. An article comprising:

a storage medium having instructions stored thereon executable by a special purpose computing platform to;

electronically calculate, using at least one ranking function corresponding to at least one ranking-sensitive query topic, a relevance score for one or more documents received in response to digital signals representing a query based, at least in part, on a measure of correlation between said at least one ranking-sensitive query topic and said query.

16. The article of claim 15, wherein said storage medium further includes instructions to electronically determining an adjusted ranking score for said one or more documents by aggregating said calculated relevance scores.

17. The article of claim 15, wherein said measure of correlation comprises a statistical probability of said query belonging to said at least one ranking-sensitive query topic.

18. An apparatus comprising:

a computing platform enabled to;

electronically identify a plurality of ranking-sensitive query topics; and

concurrently train a plurality of ranking functions associated with said plurality of ranking-sensitive query topics based, at least in part, on an application of a loss function, wherein at least one ranking function of said plurality of ranking functions corresponds to at least one ranking-sensitive query topic.

19. The apparatus of claim 18, wherein said computing platform being enabled to said electronically identify a plurality of ranking-sensitive query topics is enabled to:

electronically generate one or more query features based, at least in part, on ranking features received in response to one or more digital signals representing training queries, wherein said ranking features comprise one or more feature vectors associated with said one or more training queries; and

establish one or more clusters representative of said plurality of ranking-sensitive query topics based, at least in part, on one or more machine-learned functions.

20. The apparatus of claim 18, wherein said computing platform is further enabled to electronically calculate, using at least one of said plurality of said trained ranking functions, a relevance score for one or more documents received in response to digital signals representing a query based, at least in part, on a measure of correlation between one or more of said plurality of ranking-sensitive query topics and said query, wherein said measure of correlation comprises a statistical probability of said query belonging to said one or more of said plurality of ranking-sensitive query topics.