Method and system for simulating recommendations in a social network for an offline user

• US 9,734,507 B2
• Filed: 12/20/2007
• Issued: 08/15/2017
• Est. Priority Date: 12/20/2007
• Status: Expired due to Fees

• Current Assignee: Concert Technology Corporation
• Original Assignee: Napo Enerprises, LLC (Concert Technology Corporation)
• Inventors: Amidon, Christopher M., Walsh, Richard J.
• Primary Examiner(s): Airapetian, Mila
• Application Number:
  US11/961,730
• Publication Number:
  US 20090164199A1
• Time in Patent Office: 3,526 Days
• Field of Search: 705 267
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/266 Channel or content management, e.g. generation and management of keys and entitlement messages in a conditional access system, merging a VOD unicast channel into a multicast channel
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4788 communicating with other users, e.g. chatting arrangements for providing for computer conferences, e.g. chat rooms, to substation in data switching networks H04L12/1813; distributed application using peer-to-peer [P2P] networks H04L67/104
• CPC Class Codes: H04N 21/654 Transmission by server directed to the client
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

A method and system are provided for simulating recommendations for unavailable users in a social media recommendation network. In general, the social media recommendation network is formed such that each user in the social media recommendation network sends and receives recommendations from their friends. When one of the users in the social media recommendation network is unavailable, a recommendation simulator operates to simulate recommendations from the unavailable user. When the user once again becomes available, the recommendation simulator ceases simulation of recommendations from the user.

Claims

1. A method comprising:

determining when a first user of a plurality of users forming a media recommendation network is unavailable; and

because the first user is unavailable simulating to a second user of the plurality of users, via a computing device, recommendations from the first user while the first user is unavailable;

wherein the plurality of users is associated with a plurality of recommendation clients, and simulating recommendations from the first user comprises simulating a recommendation client from the plurality of recommendation clients associated with the first user;

wherein simulating the recommendation client associated with the first user comprises;

selecting a media item from a plurality of media items to provide a selected media item;

simulating playback of the selected media item; and

sending a recommendation for the selected media item, the recommendation being provided to at least one recommendation client of the plurality of recommendation clients associated with at least one user of the plurality of users that is identified as a friend of the first user;

wherein selecting the media item from the plurality of media items comprises;

scoring each of the plurality of media items as a function of user preferences of the first user to provide a score for each of the plurality of media items; and

selecting the media item from the plurality of media items as a function of the scores of the plurality of media items;

wherein simulating the recommendation client further comprises optimizing the user preferences during simulation; and

wherein optimizing the user preferences comprises modifying the user preferences as a function of user preferences of at least one user of the plurality of users that directly or indirectly receives recommendations from the recommendation client associated with the first user.

2. The method of claim 1 wherein simulating the recommendation client comprises:

obtaining a client context of the recommendation client, the client context being information needed in order to simulate the recommendation client; and

simulating the recommendation client using the client context.

3. The method of claim 1 wherein simulating the recommendation client comprises:

obtaining a client context of the recommendation client, the client context being information needed in order to simulate the recommendation client;

optimizing the client context to provide an optimized client context; and

simulating the recommendation client using the optimized client context.

4. The method of claim 3 wherein optimizing the client context comprises optimizing the client context as a function of historical information regarding operation of the recommendation client.

5. The method of claim 3 wherein optimizing the client context comprises optimizing the client context as a function of user preferences of at least one user of the plurality of users that directly or indirectly receives recommendations from the recommendation client associated with the first user.

6. The method of claim 3 wherein optimizing the client context comprises optimizing the client context as a function of media content currently being played by at least one user of the plurality of users that directly or indirectly receives recommendations from the recommendation client associated with the first user.

7. The method of claim 3 wherein optimizing the client context comprises optimizing the client context as a function of requests received from at least one user of the plurality of users that directly or indirectly receives recommendations from the recommendation client associated with the first user.

8. The method of claim 1 wherein optimizing the user preferences comprises optimizing the user preferences as a function of historical information regarding operation of the recommendation client of the first user.

9. The method of claim 1 wherein the user preferences are a user preference set from a plurality of user preference sets associated with the first user, and optimizing the user preferences comprises selecting one of the plurality of user preference sets to be used as the user preferences for simulation as a function of historical information regarding operation of the recommendation client of the first user.

10. The method of claim 1 wherein optimizing the user preferences comprises optimizing the user preferences as a function of media content currently being played by at least one user of the plurality of users that directly or indirectly receives recommendations from the recommendation client associated with the first user.

11. The method of claim 1 wherein optimizing the user preferences comprises modifying the user preferences as a function of requests received from at least one user of the plurality of users that directly or indirectly receives recommendations from the recommendation client associated with the first user.

12. The method of claim 1 wherein effecting sending of the recommendation comprises effecting sending of the recommendation at a time selected from the group consisting of:

a time at which simulated playback of the selected media item begins, a time during simulated playback of the selected media item, and a time at which simulated playback of the selected media item completes.

13. The method of claim 1 wherein simulating the recommendation client associated with the first user comprises receiving at least one recommendation from the at least one recommendation client of the plurality of recommendation clients associated with the at least one user of the plurality of users that is identified as a friend of the first user.

14. The method of claim 13 wherein simulating the recommendation client associated with the first user further comprises:

selecting a media item from the plurality of media items to provide a selected media item, the plurality of media items comprising at least one media item identified by the at least one recommendation and a plurality of media items in a media collection of the first user;

simulating playback of the selected media item; and

sending a recommendation for the selected media item, the recommendation being provided to at least one recommendation client of the plurality of recommendation clients associated with at least one user of the plurality of users that is identified as a friend of the first user.

21. The method of claim 1 further comprising terminating simulated recommendations from the first user if the first user is available.

15. A system comprising:

a communication interface communicatively coupling the system to a plurality of user devices via a communication network, the plurality of user devices associated with a plurality of users forming a media recommendation network; and

a control system associated with the communication interface and adapted to;

determine when a first user of the plurality of users forming the media recommendation network is unavailable; and

because the first user is unavailable, simulate recommendations from the first user to a second user while the first user is unavailable.

16. The system of claim 15 wherein the plurality of users are associated with a plurality of recommendation clients, and in order to simulate recommendations from the first user, the control system is further adapted to simulate a recommendation client from the plurality of recommendation clients associated with the first user.

17. The system of claim 16 wherein in order to simulate the recommendation client, the control system is further adapted to:

obtain a client context of the recommendation client, the client context being information needed in order to simulate the recommendation client; and

simulate the recommendation client using the client context.

18. The system of claim 16 wherein in order to simulate the recommendation client, the control system is further adapted to:

obtain a client context of the recommendation client, the client context being information needed in order to simulate the recommendation client;

optimize the client context to provide an optimized client context; and

simulate the recommendation client using the optimized client context.

19. The system of claim 16 wherein in order to simulate the recommendation client associated with the first user, the control system is further adapted to:

select a media item from a plurality of media items to provide a selected media item;

simulate playback of the selected media item; and

send a recommendation for the selected media item to at least one recommendation client of the plurality of recommendation clients associated with at least one user of the plurality of users that is identified as a friend of the first user.

20. The system of claim 16 wherein in order to simulate the recommendation client associated with the first user, the control system is further adapted to receive at least one recommendation from at least one recommendation client of the plurality of recommendation clients associated with at least one user of the plurality of users that is identified as a friend of the first user.

22. The system of claim 15 wherein the control system is further adapted to terminate simulated recommendations from the first user if the first user is available.

23. A method comprising:

determining when a first user of a plurality of users forming a media recommendation network is unavailable; and

because the first user is unavailable simulating to a second user of the plurality of users, via a computing device, recommendations from the first user while the first user is unavailable;

wherein the plurality of users is associated with a plurality of recommendation clients, and simulating recommendations from the first user comprises simulating a recommendation client from the plurality of recommendation clients associated with the first user;

wherein simulating the recommendation client associated with the first user comprises;

selecting a media item from a plurality of media items to provide a selected media item;

simulating playback of the selected media item; and

sending a recommendation for the selected media item, the recommendation being provided to at least one recommendation client of the plurality of recommendation clients associated with at least one user of the plurality of users that is identified as a friend of the first user;

wherein selecting the media item from the plurality of media items comprises;

scoring each of the plurality of media items as a function of user preferences of the first user to provide a score for each of the plurality of media items; and

selecting the media item from the plurality of media items as a function of the scores of the plurality of media items;

wherein simulating the recommendation client further comprises optimizing the user preferences during simulation; and

wherein optimizing the user preferences comprises modifying the user preferences as a function of requests received from at least one user of the plurality of users that directly or indirectly receives recommendations from the recommendation client associated with the first user.