METHOD OR SYSTEM FOR CONTENT RECOMMENDATIONS

• US 20130179252A1
• Filed: 01/11/2012
• Published: 07/11/2013
• Est. Priority Date: 01/11/2012
• Status: Active Application

• Current Assignee: Yahoo Assets LLC
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Dong, Anlei, Bian, Jiang, Reddy, Srihari, Chang, Yi, He, Xiaofeng
• Application Number:
  US13/348,573
• Publication Number:
  US 20130179252A1
• Time in Patent Office: Days
• US Class Current: 705/14.43
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Methods and systems are provided that may be utilized to recommend content to a user.

Claims

1. A method of determining one or more content recommendations other than for a search engine recommendation comprising:

measuring content selection of one or more users;

segmenting said one or more users into one or more cluster segments of a plurality of clusters based at least in part on the measured content selection; and

determining said one or more content recommendations for said one or more users from a set of content items based at least in part on the measured content selection and said one or more cluster segments.

2. The method of claim 1, wherein said determining comprises determining said one or more content recommendations to improve click through rate (CTR).

3. The method of claim 1, wherein said determining comprises determining said one or more content recommendations to improve generated advertising revenue.

4. The method of claim 1, said measuring content selection of one or more users comprises online real-time learning;

and wherein said determining comprises determining said one or more content recommendations based at least in part on said online real-time learning.

5. The method of claim 4, wherein said online real-time learning comprises online real-time learning for said one or more cluster segments;

and wherein said determining comprises determining said one or more content recommendations based at least in part on said online real-time learning for said one or more cluster segments.

6. The method of claim 5, wherein said online real-time learning for said one or more cluster segments comprises measuring dynamic CTR.

7. The method of claim 6, wherein measuring dynamic CTR comprises measuring approximately real-time users of said one or more cluster segments selecting a hyperlink to specified online content.

8. The method of claim 1, wherein segmenting said one or more users includes segmentation into a cluster of pseudo-randomly selected users.

9. The method of claim 1, wherein said measuring content selection of one or more users further comprises measuring user engagement.

10. The method of claim 9, wherein said measuring user engagement comprises measuring at least one of the following:

specific user action or specific user inaction.

11. The method of claim 10, wherein measuring specific user action comprises measuring at least one of the following:

selecting a hyperlink to specific content or user action other than selecting a hyperlink to specific content.

12. The method of claim 1, wherein said segmenting comprises segmenting users based at least in part on k means clustering or based at least in part on tensor segmentation.

13. The method of claim 1, wherein said measuring content selection of one or more users further comprises adjusting for position bias.

14. An apparatus comprising:

a computing platform;

said computing platform to;

measure content selection of one or more users, segment said one or more users into one or more cluster segments of a plurality of clusters based at least in part on the measured content selection, and determine said one or more content recommendations for said one or more users from a set of content items based at least in part on the measured content selection and said one or more cluster segments.

15. The apparatus of claim 14, wherein said computing platform to measure content selection of one or more users comprise a computing platform to further measure user engagement.

16. The apparatus of claim 15, wherein said computing platform to measure user engagement comprises a computing platform to further measure at least one of the following:

specific user action or specific user inaction.

17. The apparatus of claim 16, wherein said computing platform to measure specific user action comprises a computing platform to further measure at least one of the following:

selecting a hyperlink to specific content or user action other than selecting a hyperlink to specific content.

18. An article comprising:

a storage medium having stored thereon instructions capable of being executed by a computing platform to;

measure content selection of one or more users, segment said one or more users into one or more cluster segments of a plurality of clusters based at least in part on the measured content selection, and determine said one or more content recommendations for said one or more users from a set of content items based at least in part on the measured content selection and said one or more cluster segments.

19. The article of claim 18, wherein said instructions capable of being executed to measure content selection of one or more users further comprise instructions to measure user engagement.

20. The article of claim 19, wherein said instructions capable of being exectued to measure user engagement further comprise instructions to measure at least one of the following:

selecting a hyperlink to specific content or user action other than selecting a hyperlink to specific content.