Sharing Recommendation Agents

• US 20140032358A1
• Filed: 07/24/2013
• Published: 01/30/2014
• Est. Priority Date: 07/25/2012
• Status: Abandoned Application

• Current Assignee: ARO, Inc. (Dibona Holding)
• Original Assignee: ARO, Inc. (Dibona Holding)
• Inventors: Perkowitz, Michael, Eustice, Kevin Francis, Hickl, Andrew F.
• Application Number:
  US13/950,177
• Publication Number:
  US 20140032358A1
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

A customized recommendation agent for the user is built using a behavioral model. The customized recommendation agent selects recommendations from a corpus to present to the user, based on the behavioral model and the user'"'"'s current or predicted future context. The customized recommendation agent can be shared by the user with others, thus allowing others to access recommendations that may appeal to the user, for example, for use in planning joint activities. Because the user'"'"'s recommendation agent is independent from the user'"'"'s actual history, preferences can be shared without revealing a user'"'"'s specific behavior.

Claims

1. A method of generating a sharable customized recommendation agent for a user, comprising:

creating a recommendation agent for a user from a behavioral model;

receiving a request from the user to share the user'"'"'s recommendation agent with a recipient;

receiving the recipient'"'"'s request to follow the user'"'"'s recommendation agent; and

providing a recommendation to the recipient based on the user'"'"'s recommendation agent.

2. The method of claim 1, further comprising receiving edits to input data for the behavioral model from the user in advance of creating the recommendation agent for the user.

3. The method of claim 2, wherein the edits to input data for the behavioral model comprises deletions of data to preserve the user'"'"'s privacy.

4. The method of claim 1, wherein the behavioral model comprises at least one selected from a group consisting of a routine model and a personality model.

5. The method of claim 1, further comprising establishing behavior filters for updates to the behavioral model from which the recommendation agent for the user is created.

6. The method of claim 5, wherein the behavior filters comprise at least one selected from a group consisting of behavior from a particular city, behavior from a particular neighborhood, visits to a category of places, and behavior from a particular time of day.

7. The method of claim 1, further comprising:

defining prompts for executing the user'"'"'s recommendation agent to provide a recommendation; and

executing the user'"'"'s recommendation agent when the user has applicable expertise to the recipient'"'"'s current context.

8. The method of claim 7, wherein the user has applicable expertise to the recipient'"'"'s current context when the recipient is in a neighborhood that the user knows well.

9. The method of claim 1, wherein providing a recommendation to the recipient based on the user'"'"'s recommendation agent comprises providing an indication that the user would enjoy the recommended item based on inferences from the user'"'"'s behavioral model.

10. The method of claim 1, wherein creating a recommendation agent for a user from a behavioral model comprises using a behavioral model artificially constructed to model a certain kind of desired behavior.

11. The method of claim 1, wherein creating a recommendation agent for a user from a behavioral model comprises using a behavioral model constructed from the models of multiple users that display a certain kind of desired behavior.

12. A non-transitory computer-readable storage medium having computer program instructions embodied therein for generating a sharable customized recommendation agent for a user, the computer program instructions comprising instructions for:

creating a recommendation agent for a user from a behavioral model;

receiving a request from the user to share the user'"'"'s recommendation agent with a recipient;

receiving the recipient'"'"'s request to follow the user'"'"'s recommendation agent; and

providing a recommendation to the recipient based on the user'"'"'s recommendation agent.

13. The computer-readable storage medium of claim 12, the instructions further comprising instructions for receiving edits to input data for the behavioral model from the user in advance of creating the recommendation agent for the user.

14. The computer-readable storage medium of claim 13, wherein the edits to input data for the behavioral model comprises deletions of data to preserve the user'"'"'s privacy.

15. The computer-readable storage medium of claim 12, wherein the behavioral model comprises at least one selected from a group consisting of a routine model and a personality model.

16. The computer-readable storage medium of claim 12, the instructions further comprising instructions for establishing behavior filters for updates to the behavioral model from which the recommendation agent for the user is created.

17. The computer-readable storage medium of claim 16, wherein the behavior filters comprise at least one selected from a group consisting of behavior from a particular city, behavior from a particular neighborhood, visits to a category of places, and behavior from a particular time of day.

18. The computer-readable storage medium of claim 12, the instructions further comprising instructions for:

defining prompts for executing the user'"'"'s recommendation agent to provide a recommendation; and

executing the user'"'"'s recommendation agent when the user has applicable expertise to the recipient'"'"'s current context.

19. The computer-readable storage medium of claim 18, wherein the user has applicable expertise to the recipient'"'"'s current context when the recipient is in a neighborhood that the user knows well.

20. The computer-readable storage medium of claim 12, wherein the instructions for providing a recommendation to the recipient based on the user'"'"'s recommendation agent comprise instructions for providing an indication that the user would enjoy the recommended item based on inferences from the user'"'"'s behavioral model.

21. The computer-readable storage medium of claim 12, wherein the instructions for creating a recommendation agent for a user from a behavioral model comprise instructions for using a behavioral model artificially constructed to model a certain kind of desired behavior.

22. The computer-readable storage medium of claim 12, wherein the instructions for creating a recommendation agent for a user from a behavioral model comprise instructions for using a behavioral model constructed from the models of multiple users that display a certain kind of desired behavior.