MEDIA ASSET RECOMMENDATION SERVICE

• US 20100318919A1
• Filed: 06/16/2009
• Published: 12/16/2010
• Est. Priority Date: 06/16/2009
• Status: Active Grant

• Current Assignee: Rovi Technologies Corporation (Adeia Inc.)
• Original Assignee: Microsoft Corporation
• Inventors: Weare, Christopher B., Gibson, Chad C., Evans, Christopher A., Murphy, Shawn M.
• Granted Patent: US 9,460,092 B2
• Time in Patent Office: Days
• US Class Current: 715/745
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/48 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/639 using playlists
• CPC Class Codes: G06F 16/735 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04N 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score

Abstract

A media asset recommendation service is described. In embodiments, friends of a user are identified in a social graph that is associated with the user, and interactions with media assets by the friends of the user are compiled. Friend interaction data for each of the media assets can be determined from the media assets, the social graph, and the interactions with the media assets by the friends of the user. When a selection of one of the media assets is received from the user via a user device, the friend interaction data that is associated with the media asset selected by the user is communicated to the user device.

Claims

1. A computer-implemented method initiated at a user device, the method comprising:

receiving a selection of a media asset that is selected by a user;

receiving friend interaction data associated with the media asset that is selected by the user, the friend interaction data including a list of friends of the user that have interacted with the media asset and interactions with the media asset by each of the friends; and

displaying an asset recommendation interface on a display of the user device, the asset recommendation interface including the list of friends and the interactions by each of the friends with the media asset.

2. The computer-implemented method as recited in claim 1, wherein the friends of the user in the list are selected from a social graph that is associated with the user.

3. The computer-implemented method as recited in claim 1, wherein the friends of the user in the list are displayed in the asset recommendation interface in a descending order based on an interaction rating corresponding to each friend, the interaction rating determined from the interactions with the media asset by each friend of the user in the list.

4. The computer-implemented method as recited in claim 1, wherein the asset recommendation interface further includes a prediction rating that indicates a likelihood the user will like the media asset based at least in part on the interactions with the media asset by the friends of the user in the list.

5. The computer-implemented method as recited in claim 1, wherein the media asset is a digital music file of a song.

6. The computer-implemented method as recited in claim 5, wherein the interactions with the media asset include at least one of purchasing the digital music file, downloading the digital music file, rendering the song, or discussing the song by the friends of the user.

7. The computer-implemented method as recited in claim 1, wherein the asset recommendation interface further includes a user similarity rating between the user and each of the friends of the user in the list, the user similarity rating determined from a similarity between the interactions with media assets by the friends of the user and interactions with media assets by the user.

8. A computer-implemented method, comprising:

compiling interactions with media assets by friends of a user, the friends being identified in a social graph that is associated with the user;

generating friend interaction data for each of the media assets by aggregating the interactions with the media assets by the friends of the user;

receiving a selection of a media asset from the user via a user device; and

communicating the friend interaction data that is associated with the media asset selected by the user to the user device.

9. The computer-implemented method as recited in claim 8, wherein the friend interaction data includes a list of friends of the user that have interacted with the media asset.

10. The computer-implemented method as recited in claim 9, wherein the friends of the user are sorted in a descending order based on an interaction rating corresponding to each friend, the interaction rating determined from the interactions with the media asset by each friend in the list.

11. The computer-implemented method as recited in claim 9, wherein the friend interaction data includes the interactions with the media asset by each friend in the list.

12. The computer-implemented method as recited in claim 9, wherein the list of friends is generated by assigning a higher weight to interactions by friends that have a higher user similarity rating, the user similarity rating determined from a similarity between the interactions with the media asset by the friends of the user and interactions with the media asset by the user.

13. The computer-implemented method as recited in claim 12, wherein the list of friends further includes a prediction rating that indicates a likelihood that the user will like the media asset based at least in part on the interactions with the media asset by the friends of the user and the user similarity ratings of the friends of the user.

14. The computer-implemented method as recited in claim 8, wherein the media asset is a digital music file of a song.

15. The computer-implemented method as recited in claim 14, wherein the interactions with the media asset include at least one of purchasing the digital music file, downloading the digital music file, rendering the song, or discussing the song by the friends of the user.

16. A recommendation system, comprising:

a social graph that indicates relationships between a user and friends of the user;

a database of media assets that can be requested by the user and the friends of the user via user devices;

a database of interactions with the media assets by the user and the friends of the user; and

a media asset recommendation service configured to generate friend interaction data for each media asset in the database of media assets, the friend interaction data determined from the social graph, the media assets, and the interactions with the media assets by the user and the friends of the user.

17. The recommendation system as recited in claim 16, wherein the media asset recommendation service is further configured to generate the friend interaction data to include a list of friends of the user that have interacted with each media asset, and wherein the list of friends of the user are sorted in a descending order based on an interaction rating corresponding to each friend, the interaction rating determined from the interactions with the media asset by each friend in the list.

18. The recommendation system as recited in claim 17, wherein the media asset recommendation service is further configured to generate the list of friends by assigning a higher weight to interactions by friends that have a higher user similarity rating, the user similarity rating determined from a similarity between the interactions with media assets by the friends of the user and interactions with media assets by the user.

19. The recommendation system as recited in claim 16, wherein the media assets are a digital music files of songs.

20. The recommendation system as recited in claim 16, wherein the interactions with the media assets include at least one of purchasing the digital music files, downloading the digital music files, rendering the songs, or discussing the songs by the friends of the user.