Method and apparatus for providing recommendations to a user of a cloud computing service

• US 9,317,179 B2
• Filed: 04/10/2012
• Issued: 04/19/2016
• Est. Priority Date: 01/08/2007
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Ho, Edwin
• Primary Examiner(s): Srivastava, Vivek
• Assistant Examiner(s): Asres, Hermon
• Application Number:
  US13/443,365
• Publication Number:
  US 20130218961A1
• Time in Patent Office: 1,470 Days
• Field of Search: 709/204, 709/201
• US Class Current: 1/1
• CPC Class Codes: G06F 16/178 Techniques for file synchronisation in file systems
• CPC Class Codes: G06F 16/4387 by the use of playlists
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 12/1822 Conducting the conference, e.g. admission, detection, selection or grouping of participants, correlating users to one or more conference sessions, prioritising transmission
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 65/60 Network streaming of media packets
• CPC Class Codes: H04L 65/612 for unicast
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/04 specially adapted for terminals or networks with limited capabilities; specially adapted for terminal portability
• CPC Class Codes: H04L 67/06 specially adapted for file transfer, e.g. file transfer protocol [FTP]
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1095 Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/55 Push-based network services
• CPC Class Codes: H04W 56/001 Synchronization between nodes
• CPC Class Codes: H04W 84/12 WLAN [Wireless Local Area Networks]

Abstract

A method and apparatus is disclosed for transferring digital content from a computing cloud to a computing device and generating recommendations for the user of the computing device.

Claims

1. A computing cloud comprising:

a computing device including a content store that stores multiple sets of metadata for multiple users of a social network, wherein each set of metadata corresponds to a user of the social network and comprises information relating to activity of the user and at least one digital content playlist including digital content accessed by the user; and

a recommendation engine, associated with the content store, to;

provide at least one user of the social network with information relating to one or more user interaction patterns of the user with digital content over time;

based on the multiple sets of metadata, compare an at least one digital content playlist of a first user of the social network to an at least one digital content playlist of other users of the social network to identify a second user of the social network with a digital content playlist that most closely matches the at least one digital content playlist of the first user; and

provide a recommendation for digital content to the first user based in part on a first set of metadata corresponding to the first user, a second set of metadata corresponding to the second user, the social network, and information relating to one of more user interaction patterns of the first user to digital content over time,

wherein for at least one user of the social network, information relating to one or more user interaction patterns of the user with digital content over time includes metric information identifying the one or more user interaction patterns.

2. The apparatus of claim 1, wherein:

the social network provides a social connection between the first user and the second user.

3. The apparatus of claim 1, wherein:

for at least one user of the social network, information relating to one or more user interaction patterns of the user with digital content over time includes a listening pattern.

4. The apparatus of claim 1, wherein:

the recommendation engine provides the metric information in one of the following forms;

a graph, and a pie chart.

5. The apparatus of claim 2, wherein:

for each user of the social network, the recommendation engine is further configured to provide one or more recommendations for digital content to the user based on a set of metadata for the user and at least one set of metadata for at least one other user that is socially connected with the user in the social network.

6. The apparatus of claim 1, wherein:

for at least one user of the social network, the recommendation engine is further configured to indicate one or more changes in one or more user interaction patterns of the user with digital content over time; and

each recommendation comprises one or more of the following;

an audio content recommendation, a playlist recommendation, a radio station recommendation, a video content recommendation, an advertisement recommendation, and a coupon recommendation.

7. A computing cloud comprising:

a content store that stores multiple sets of metadata for multiple users of a social network, wherein each set of metadata corresponds to a user of the social network and comprises information relating to activity of the user and at least one digital content playlist including digital content accessed by the user; and

a recommendation engine comprising;

a first engine to;

provide at least one user of the social network with information relating to one or more user interaction patterns of the user with digital content over time;

based on the multiple sets of metadata, compare an at least one digital content playlist of a first user of the social network to an at least one digital content playlist of other users of the social network to identify a second user of the social network with a digital content playlist that most closely matches the at least one digital content playlist of the first user, and

generate a set of recommendations for the first user of the social network based upon a first set of metadata corresponding to the first user, a second set of metadata corresponding to the second user, and information relating to one or more user interaction patterns of the first user with digital content over time; and

a second engine comprising a social graph filter to generate a subset of each set of recommendations,

wherein for at least one user of the social network, information relating to one or more user interaction patterns of the user with digital content over time includes metric information identifying the one or more user interaction patterns.

8. The apparatus of claim 7, wherein:

for each user of the social network, the social network provides at least one social connection between the user and at least one other user of the social network, such that each user of the social network is associated with at least one other user of the social network.

9. The apparatus of claim 7, wherein:

for at least one user of the social network, information relating to one or more user interaction patterns of the user with digital content over time includes a listening pattern.

10. The apparatus of claim 7, wherein:

the recommendation engine provides the metric information in one of the following forms;

a graph or a pie chart.

11. The apparatus of claim 8, wherein:

for each user of the social network, the recommendation engine provides one or more recommendations for digital content to the user based on a set of metadata for the user and at least one additional set of metadata for at least one other user that is socially connected with the user in the social network.

12. The apparatus of claim 7, wherein:

for at least one user of the social network, the recommendation engine indicates one or more changes in one or more user interaction patterns of the user with digital content over time; and

each subset of each set of recommendations comprises one or more of the following;

an audio content recommendation, a playlist recommendation, a radio station recommendation, a video content recommendation, an advertisement recommendation, and a coupon recommendation.

13. A method for generating recommendations comprising:

generating, at a computing device, multiple sets of metadata for multiple users of a social network, wherein each set of metadata corresponds to a user of the social network and comprises information relating to activity of the user and at least one digital content playlist including digital content accessed by the user;

providing, by a recommendation engine, at least one user of the social network with information relating to one or more user interaction patterns of the user with digital content over time;

based on the multiple sets of metadata, comparing an at least one digital content playlist of a first user of the social network to an at least one digital content playlist of other users of the social network to identify a second user of the social network with a digital content playlist that most closely matches the at least one digital content playlist of the first user; and

generating, by the recommendation engine, a recommendation for digital content to the first user based in part on a first set of metadata corresponding to the first user, a second set of metadata corresponding to the second user, the social network, and information relating to one or more user interaction patterns of the first user to digital content over time,

wherein for at least one user of the social network, information relating to one or more user interaction patterns of the user with digital content over time includes metric information identifying the one or more user interaction patterns.

14. The method of claim 13, wherein:

the social network provides a social connection between the first user and the second user.

15. The method of claim 13, wherein:

for at least one user of the social network, information relating to one or more user interaction patterns of the user with digital content over time includes a listening pattern.

16. The method of claim 13, wherein:

the recommendation engine provides the metric information in one of the following forms;

a graph or a pie chart.

17. The method of claim 14, wherein:

for each user of the social network, the recommendation engine is further configured to provide one or more recommendations for digital content to the user based on a set of metadata for the user and at least one additional set of metadata for at least one other user that is socially connected with the user in the social network.

18. The method of claim 13, wherein:

for at least one user of the social network, the recommendation engine is further configured to indicate one or more changes in one or more user interaction patterns of the user with digital content over time; and

each recommendation comprises one or more of the following;

an audio content recommendation, a playlist recommendation, a radio station recommendation, a video content recommendation, an advertisement recommendation, and a coupon recommendation.

19. A method for generating recommendations comprising:

generating, by a computing device, multiple sets of metadata for multiple users of a social network, wherein each set of metadata corresponds to a user of the social network and comprises information relating to activity of the user and at least one digital content playlist including digital content accessed by the user;

storing each set of metadata corresponding to each user of the social network in a content store of the computing device;

providing, by a recommendation engine, at least one user of the social network with information relating to one or more user interaction patterns of the user with digital content over time;

based on the multiple sets of metadata, comparing an at least one digital content playlist of a first user of the social network to at least one digital content playlist of other users of the social network to identify a second user of the social network with a digital content playlist that most closely matches the at least one digital content playlist of the first user,

generating a set of recommendations for the first user of the social network based upon a first set of metadata corresponding to the first user, a second set of metadata corresponding to the second user, and information relating to one or more user interaction patterns of the first user with digital content over time; and

applying, by the recommendation engine, a social graph filter to generate a subset of each set of recommendations,

wherein for at least one user of the social network, information relating to one or more user interaction patterns of the user with digital content over time includes metric information identifying the one or more user interaction patterns.

20. The method of claim 19, wherein:

for each user of the social network, the social network provides at least one social connection between the user and at least one other user of the social network, such that each user of the social network is associated with at least one other user of the social network.

21. The method of claim 19, wherein:

for at least one user of the social network, information relating to one or more user interaction patterns of the user with digital content over time includes a listening pattern.

22. The method of claim 19, wherein:

the recommendation engine provides the metric information in one of the following forms;

a graph or a pie chart.

23. The method of claim 20, wherein:

for each user of the social network, the recommendation engine is further configured to provide one or more recommendations for digital content to the user based on a set of metadata for the user and at least one additional set of metadata for at least one other user that is socially connected with the user in the social network.

24. The method of claim 19, wherein:

for at least one user of the social network, the recommendation engine is further configured to indicate one or more changes in one or more user interaction patterns of the user with digital content over time; and

each subset of each set of recommendations comprises one or more of the following;

an audio content recommendation, a playlist recommendation, a radio station recommendation, a video content recommendation, an advertisement recommendation, and a coupon recommendation.