Computer processes for predicting media item popularity

• US 10,546,309 B2
• Filed: 08/04/2017
• Issued: 01/28/2020
• Est. Priority Date: 09/23/2013
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: McGilliard, Christopher Lon
• Primary Examiner(s): Nguyen, Merilyn P
• Application Number:
  US15/669,830
• Publication Number:
  US 20170330209A1
• Time in Patent Office: 907 Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/635 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/68 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• 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: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: H04N 21/44204 Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched monitoring of user activities for profile generation for accessing a video database G06F16/739; protecting generic digital content where the protection is independent of the precise nature of the content G06F21/10; arrangements for monitoring the use made of the broadcast services in broadcast systems H04H60/31

Abstract

Systems and methods are disclosed that identify users of a media distribution system that tend to consume popular media items prior to such media items gaining popularity. For example, a set of early adopters may be identified that tend to listen to music associated with particular artists before such artists become popular. The systems and methods disclosed may also utilize identified early adopters to determine relatively obscure or unpopular media items (or creators thereof) that are likely to become popular in the future. Illustratively, an obscure artist whose content is commonly consumed by early adopters can be identified as potentially achieving widespread popularity in the future. These media items predicted to become popular or media item creators may then be recommended to other users of the media distribution system.

Claims

1. A computer-implemented method comprising:

retrieving media interaction information reflecting user interaction with a set of popular media items, wherein popular media items are associated with satisfaction of a set of popularity criteria;

determining a set of users that interacted with individual media items of the set of popular media items prior to popularity of respective individual media items, wherein the set of users are determined based at least in part on the media interaction information;

assigning scores to individual users of the set of users, wherein a score of an individual user is based at least in part on a length of time between i) an initial interaction of the individual user with the respective individual media items and ii) a point in time at which the respective individual media items satisfied the set of popularity criteria;

retrieving user interaction information reflecting interactions of the set of users with a set of media items not yet popular on a music distribution system;

ranking the set of media items that are not yet popular based at least in part on the scores of the individual users and the user interaction information reflecting interactions of the set of users;

generating, based on rankings of the set of media items, a recommendation to at least one user of the music distribution system for a media item of the set of media items that are not yet popular; and

transmitting the recommendation to a computing device of the at least one user;

wherein said method is performed entirely by a computer system that comprises one or more computing devices.

2. The computer-implemented method of claim 1, wherein the media item is at least one of a song, an album, a video, a book, a movie, or a collection thereof.

3. The computer-implemented method of claim 1, wherein popular media items are associated with at least one of current or historical satisfaction of the set of popularity criteria.

4. The computer-implemented method of claim 1 wherein the recommendation is transmitted via at least one of a display page output to the computing device of the at least one user or an electronic message transmitted to the computing device of the at least one user.

5. The computer-implemented method of claim 1, wherein the ranking of the set of media items is further based at least in part on at least one of a level of interaction with individual media items of the set of media items by individual users of the set of users, a location of a creator associated with individual media items of the set of media items, or a location of individual users of the set of users.

6. The computer-implemented method of claim 1, wherein the scores of individual users is further based at least in part on a number of interactions of an individual user of with respective popular media items of the set of popular media items.

7. A system comprising:

a data store including media interaction information reflecting user interaction with a set of popular media items, wherein popular media items are associated with satisfaction of a set of popularity criteria;

one or more processors in communication with the data store and configured with specific computer-executable instructions to;

determine a set of users that interacted with individual media items of the set of popular media items prior to popularity of respective individual media items, wherein the set of users are determined based at least in part on the media interaction information;

assign scores to individual users of the set of users, wherein a score of an individual user is based at least in part on a length of time between i) an initial interaction of the individual user with the respective individual media items and ii) a point in time at which the respective individual media items satisfied the set of popularity criteria;

retrieve user interaction information reflecting interactions of the set of users with a set of media items not yet popular on a music distribution system;

rank the set of media items that are not yet popular based at least in part on the scores of the individual users and the user interaction information reflecting interactions of the set of users;

generate, based on rankings of the set of media items, a recommendation to at least one user of the music distribution system for at least one media item of the set of media items that are not yet popular; and

transmit the recommendation to a computing device of the at least one user.

8. The system of claim 7, wherein the set of popularity criteria comprise at least one of a level of interaction with an individual media item, a level of interaction with a creator of the individual media item, an externally published popularity ranking of the creator, or reported sales of the creator by an external organization.

9. The system of claim 8, wherein the level of interaction with the creator includes at least one of a number of times users have consumed content items associated with the creator, viewed information regarding the creator or content items associated with the creator, searched for the creator or content items associated with the creator, commented on the creator or content items associated with the creator, reviewed the creator or content items associated with the creator, or rated the creator or content items associated with the creator.

10. The system of claim 8, wherein the level of interaction with the creator corresponds to interactions on a social networking system, and wherein the level of interaction with the creator includes at least one of a number of posts regarding the creator on the social networking system, a number of users following the creator on the social networking system, or a number of endorsements of the creator on the social networking system.

11. The system of claim 7, wherein the ranking of the set of media items is further based at least in part on at least one of a level of interaction with individual media items of the set of media items by individual users of the set of users, a location of a creator associated with individual media items of the set of media items, or a location of individual users of the set of users.

12. The system of claim 7, wherein the scores of individual users is further based at least in part on a number of interactions of an individual user of with respective popular media items of the set of popular media items.

13. A non-transitory computer-readable medium comprising computer-executable instructions for identifying media items predicted to become popular, wherein the computer-executable instructions, when executed by a computing system, cause the computing system to:

retrieve media interaction information reflecting user interaction with a set of popular media items, wherein popular media items are associated with satisfaction of a set of popularity criteria;

determine a set of users that interacted with individual media items of the set of popular media items prior to popularity of respective individual media items, wherein the set of users are determined based at least in part on the media interaction information;

assign scores to individual users of the set of users, wherein a score of an individual user is based at least in part on a length of time between i) an initial interaction of the individual user with the respective individual media items and ii) a point in time at which the respective individual media items satisfied the set of popularity criteria;

retrieve user interaction information reflecting interactions of the set of users with a set of media items not yet popular on a music distribution system;

rank the set of media items that are not yet popular based at least in part on the scores of the individual users and the user interaction information reflecting interactions of the set of users;

generate, based on rankings of the set of media items, a recommendation to at least one user of the music distribution system for at least one media item of the set of media items that are not yet popular; and

transmit the recommendation to a computing device of the at least one user.

14. The non-transitory computer-readable medium of claim 13, wherein the recommendation of the set of media items that are not yet popular corresponds to a playlist including the set of media items that are not yet popular.

15. The non-transitory computer-readable medium of claim 13, wherein the score of an individual user is further based at least in part on a geographic location associated with the individual user.

16. The non-transitory computer-readable medium of claim 13, wherein the one or more popular media items are associated with a specific genre of media item.

17. The non-transitory computer-readable medium of claim 13, wherein the set of popularity criteria include at least one of an absolute threshold value or a threshold value relative to popularity metrics of other media items.

18. The non-transitory computer-readable medium of claim 13, wherein the set of popularity criteria establish a required change in a popularity metric over a period of time.

19. The non-transitory computer-readable medium of claim 13, wherein the ranking of the set of media items is further based at least in part on at least one of a level of interaction with individual media items of the set of media items by individual users of the set of users, a location of a creator associated with individual media items of the set of media items, or a location of individual users of the set of users.