Blended ranking of dissimilar populations using an N-furcated normalization technique

• US 9,058,332 B1
• Filed: 12/28/2012
• Issued: 06/16/2015
• Est. Priority Date: 05/04/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Darby, Matthew Thomas, Karuturi, Naga Naresh
• Primary Examiner(s): SYED, FARHAN M
• Application Number:
  US13/729,158
• Time in Patent Office: 900 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/438 Presentation of query results
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

Systems and methods for blending dissimilar, ordered populations into a single selection for users are disclosed herein. In an aspect, content items belonging to distinct parent populations which display a large disparity in the value which is used for ranking purposes, can be displayed together in a single continuously ranked list for simple browsing and selection by users. Further, a score can be assigned to the respective media content items based at least in part on a median value of a distribution of media content items corresponding to the respective parent populations and this score can be used as a normalized, universal value with which to rank content from all dissimilar populations together.

Claims

1. A system comprising:

a memory having stored thereon computer executable components; and

a processor configured to execute the following computer executable components stored in the memory;

an analysis component that analyzes social network tags related to media content items belonging to respective parent populations based at least in part on user browsing information to determine user interest of the media content items, wherein the respective parent populations contain statistical information associated with the population of media content items, the statistical information comprising at least organization and interpretation of data within the population;

a scoring component that assigns respective scores to the media content items based at least in part on a distribution of media content items corresponding to the respective parent populations according to the following;

determine respective view counts for media content items of the parent populations;

group respective view counts into buckets by powers of ten for the parent populations;

identify respective buckets for parent populations having a maximum count value;

determine respective geometric means of the respective buckets for parent populations having the maximum count value;

determine respective offsets for the parent populations based upon respective differences between the geometric means; and

determine the respective scores of media content items based upon the respective offsets of the parent populations; and

a ranking component that ranks the media content items based at least in part on the respective scores; and

an ordering component that aggregates the media content items belonging to the respective parent populations into an ordered list based at least in part on the ranking.

2. The system of claim 1, wherein the analysis component employs a statistical generator component that generates statistics associated with media content items corresponding to a respective parent population.

3. The system of claim 1 comprising a presentation component that generates a display of the ordered media content items.

4. The system of claim 1, wherein the media content items are at least one of movie items, audio items, image items, downloadable applications, or other downloadable software or media.

5. The system of claim 1, wherein at least one parent population comprises free media content items.

6. The system of claim 1, wherein at least one parent population comprises paid-for media content items.

7. The system of claim 1, wherein the view count represents at least one of consumption of a media content item, a click of the media content item, a download of the media content item, or an installation of the media content item.

8. The system of claim 1, wherein the ordering component further lists a blend of the media content items belonging to the respective parent populations with dissimilar distributions based at least on respective median view count values of the respective parent populations.

9. The system of claim 1, wherein the ranking component ranks the media content items further based at least in part on at least one of critical rating, number of critical reviews, recency, user preference, user traffic, popularity, or user demographics.

10. The system of claim 1, comprising an advertisement component that applies an advertisement to a section of the list.

11. The system of claim 1, wherein the distribution of media content items is a median distribution.

12. A method, comprising:

analyzing, by a system including a processor, social network tags related to media content items belonging to respective parent populations based at least in part on user browsing information to determine user interest of the media content items, wherein the respective parent populations contain statistical information associated with the population of media content items, the statistical information comprising at least organization and interpretation of data within the population;

assigning, by the system, respective scores to the media content items based at least in part on a distribution of media content items corresponding to the respective parent populations, the assigning comprising;

ascertaining respective view counts for media content items of the parent populations;

grouping respective view counts into buckets by powers of ten for the parent populations;

identifying respective buckets for parent populations having a maximum count value;

ascertaining respective geometric means of the respective buckets for parent populations having the maximum count value;

ascertaining respective offsets for the parent populations based upon respective differences between the geometric means; and

generating the respective scores of media content items based upon the respective offsets of the parent populations; and

ranking, by the system, the media content items based at least in part on the score; and

consolidating, by the system, the media content items belonging to the respective parent populations into an ordered list based at least in part on the ranking.

13. The method of claim 12, further comprising generating statistics associated with the respective parent population and assigning the respective scores further based upon the statistics.

14. The method of claim 12, further comprising generating, by the system, a display of the ordered media content items.

15. The method of claim 12, wherein media content items are at least one of a movie item, audio item, image item, downloadable application, or other downloadable software or media.

16. The method of claim 12, wherein at least one parent population comprises free media content items.

17. The method of claim 12, wherein the at least one parent population comprises paid-for media content items.

18. The method of claim 12, wherein the view count represents at least one of consumption of a media content item, a click of the media content item, a download of the media content item, or an installation of the media content item.

19. The method of claim 12, further comprising listing, by the system, a blend of the media content items belonging to the respective parent populations with dissimilar distributions based at least on respective median view count values of the parent populations.

20. The method of claim 12, further comprising ranking the media content items further based at least in part on at least one of critical rating, number of critical reviews, recency, user interest, user preference, user traffic, popularity, or user demographics.

21. The method of claim 12, further comprising applying, by the system, an advertisement to a section of the list.

22. A non-transitory computer-readable medium having instructions stored thereon that, in response to execution, cause a system including a processor to perform operations comprising:

analyzing social network tags related to media content items belonging to respective parent populations based at least in part on user browsing information to determine user interest of the media content items, wherein the respective parent populations contain statistical information associated with the population of media content items, the statistical information comprising at least organization and interpretation of data within the population;

assigning respective scores to the media content items based at least in part on a distribution of media content items corresponding to the respective parent populations, the assigning comprising;

determining respective view counts for media content items of the parent populations;

grouping respective view counts into buckets by powers of ten for the parent populations;

identifying respective buckets for parent populations having a maximum count value;

determining respective geometric means of the respective buckets for parent populations having the maximum count value;

determining respective offsets for the parent populations based upon respective differences between the geometric means;

determining the respective scores of media content items based upon the respective offsets of the parent populations; and

ranking the media content items based at least in part on the score; and

accumulating the media content items belonging to the respective parent populations into an ordered list based at least in part on the ranking.