METHOD AND APPARATUS FOR A RANKING ENGINE

• US 20080201323A1
• Filed: 01/28/2008
• Published: 08/21/2008
• Est. Priority Date: 11/22/2004
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: AOL LLC (Apollo Global Management, Inc.)
• Inventors: Tuttle, Timothy D., Kocks, Peter F., Beguelin, Adam L.
• Granted Patent: US 7,912,836 B2
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/284 Relational databases
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/438 Presentation of query results
• CPC Class Codes: G06F 16/735 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/738 Presentation of query results
• CPC Class Codes: G06F 16/783 using metadata automatically derived from the content
• 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 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: Y10S 707/914 Video
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99937 Sorting

Abstract

A computer-implemented method is provided for ranking files from an Internet search. In one embodiment, the method comprises assigning a score to each file based on at least one of the following factors: recency, editorial popularity, clickthru popularity, favorites metadata, or favorites collaborative filtering. The file may be organized based on the assigned scores to provide users with more accurate search results.

Claims

1. A computer-implemented method for organizing a collection of files from an Internet search, the method comprising:

assigning a score to each file based on at least the following factors;

recency, editorial popularity, clickthru popularity, favorites metadata, and favorites collaborative filtering;

organizing the files based on the assigned scores; and

displaying the files as organized.

2. The method of claim 1 wherein the score is R_T and is determined using the following formula:

3. The method of claim 2 wherein recency is weighted based on the following formula for R_r:

4. The method of claim 2 wherein editorial popularity is weighted between 1 and 0 and is based on at least one of the following:

Neilsen ratings, known brand names, website popularity (e.g. Alexa ranking), or the judgment of a professional or corporation with expertise in online media.

5. The method of claim 2 wherein clickthru popularity is weighted based on the following formula for R_c:

R_c=W_cpmR_cpm+W_cphR_cph+W_cpdRcpd

where;

6. The method of claim 1 wherein weighting of favorites metadata is R_md=0 if no matches are found or 1 if a keyword field in the metadata of the file matches any favorite titles in a user'"'"'s favorite titles file, any favorite people in a user'"'"'s favorite people file, or any keyword in a user'"'"'s favorite keywords file.

7. The method of claim 1 wherein weighting of collaborative filtering favorites metadata is R_cf

R_cf,l=W_sim(S_max,l)+(1−

W_sim)P_l,

where;

8. The method of claim 8 wherein R_cf is a weighted sum of the maximum user similarity for an item l and the popularity of item l among K-Nearest Neighbors such that 0≦

R_cf≦

1.

9. A computer-implemented method for organizing a collection of files from an Internet search, the method comprising:

assigning a score to each file based on favorites collaborative filtering W_cfR_cf and at least one of the following factors;

recency W_rR_r, editorial popularity W_eR_e, clickthru popularity W_cR_c, and favorites metadata W_mdR_md;

organizing the files based on the assigned scores; and

displaying the files as organized.

10. The method of claim 9 wherein the score is R_T and is determined using the following formula:

11. The method of claim 10 wherein recency is weighted based on the following formula for R_r:

12. The method of claim 10 wherein editorial popularity is weighted between 1 and 0 and is based on at least one of the following:

Neilsen ratings, known brand names, website popularity (e.g. Alexa ranking), or the judgment of a professional or corporation with expertise in online media.

13. The method of claim 10 wherein clickthru popularity is weighted based on the following formula for R_c:

R_c=W_cpmR_cpm+W_cphR_cph+W_cpdR_cpd

where;

14. The method of claim 9 wherein weighting of favorites metadata is R_md=0 if no matches are found or 1 if a keyword field in the metadata of the file matches any favorite titles in a user'"'"'s favorite titles file, any favorite people in a user'"'"'s favorite people file, or any keyword in a user'"'"'s favorite keywords file.

15. The method of claim 9 wherein weighting of collaborative filtering favorites metadata is R_cf

R_cf,l=W_sim(S_max,l)+(1−

W_sim)P_l,

where;

16. The method of claim 15 wherein R_ef is a weighted sum of the maximum user similarity for an item l and the popularity of item l among K-Nearest Neighbors such that 0≦

R_cf<

1.

17. A computer system comprising:

a ranking engine having programming code for displaying results of a search query based on scores,

wherein the scores for files found in the search are based on favorites collaborative filtering W_cfR_cf and at least one of the following factors;

recency W_rR_r, editorial popularity W_eR_e, clickthru popularity W_cR_c, and favorites metadata W_mdR_md.

18. The system of claim 17 wherein each of the scores is R_T and is determined using the following formula:

19. The system of claim 18 wherein recency is weighted based on the following formula for R_r:

20. The system of claim 18 wherein editorial popularity is weighted between 1 and and is based on at least one of the following:

Neilsen ratings, known brand names, website popularity (e.g. Alexa ranking), or the judgment of a professional or corporation with expertise in online media.

21. The system of claim 18 wherein clickthru popularity is weighted based on the following formula for R_c:

R_c=W_cpmR_cpm+W_cphR_cph+W_cpdR_cpd

where;

22. The system of claim 17 wherein weighting of favorites metadata is R_md=0 if no matches are found or 1 if a keyword field in the metadata of the file matches any favorite titles in a user'"'"'s favorite titles file, any favorite people in a user'"'"'s favorite people file, or any keyword in a user'"'"'s favorite keywords file.

23. The system of claim 17 wherein weighting of collaborative filtering favorites metadata is R_cf

R_cf,l=W_sim(S_max,l)+(1−

W_sim)P_l,

where;

W_sim=similarity weighting factor

24. The system of claim 23 wherein R_cf is a weighted sum of the maximum user similarity for an item l and the popularity of item l among K-Nearest Neighbors such that 0≦

R_cf≦

1.