User-Powered Recommendation System

• US 20100138443A1
• Filed: 11/12/2009
• Published: 06/03/2010
• Est. Priority Date: 11/17/2008
• Status: Active Grant

• Current Assignee: AT&T Intellectual Property I LP (AT&T, Inc.), Board of Regents of the University of Texas System (University of Texas System)
• Original Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Inventors: Ramakrishnan, Kadangode K., Cho, Tae Won, Srivastava, Divesh, Zhang, Yin
• Granted Patent: US 8,943,081 B2
• Time in Patent Office: Days
• US Class Current: 707/769
• CPC Class Codes: G06F 16/244 Grouping and aggregation
• 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 16/90324 using system suggestions
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G11B 27/105 of operating discs
• CPC Class Codes: H04N 21/4661 Deriving a combined profile for a plurality of end-users of the same client, e.g. for family members within a home user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4756 for rating content, e.g. scoring a recommended movie

Abstract

Recommendation systems are widely used in Internet applications. In current recommendation systems, users only play a passive role and have limited control over the recommendation generation process. As a result, there is often considerable mismatch between the recommendations made by these systems and the actual user interests, which are fine-grained and constantly evolving. With a user-powered distributed recommendation architecture, individual users can flexibly define fine-grained communities of interest in a declarative fashion and obtain recommendations accurately tailored to their interests by aggregating opinions of users in such communities. By combining a progressive sampling technique with data perturbation methods, the recommendation system is both scalable and privacy-preserving.

Claims

1. A method for generating a recommendation, the method comprising the steps of:

receiving a user-defined recommendation request;

receiving a user-defined declarative community definition;

searching at least one recommendation database;

retrieving rating data from at least one member of the community defined by the declarative community definition; and

generating at least one recommendation based at least in part on the rating data.

2. The method of claim 1, further comprising the steps of:

receiving a user-defined item constraint; and

generating at least one recommendation based at least in part on the user-defined item constraint.

3. The method of claim 1, wherein the declarative community definition comprises a predicate on user attributes.

4. The method of claim 1, wherein the step of generating at least one recommendation based at least in part on the rating data comprises the step of:

generating at least one recommendation according to an aggregation function of item attributes from the rating data.

5. The method of claim 1, further comprising the step of:

progressively sampling the rating data according to a user-defined sampling policy.

6. The method of claim 5, wherein the user-defined sampling policy comprises one of:

a user-defined sampling rate; and

a user-defined accuracy level.

7. The method of claim 1, further comprising the step of:

perturbing the rating data.

8. The method of claim 1, further comprising the steps of:

receiving a user-defined recommendation algorithm; and

generating at least one recommendation based at least in part on the user-defined recommendation algorithm.

9. An apparatus for generating a recommendation, the apparatus comprising:

means for receiving a user-defined recommendation request;

means for receiving a user-defined declarative community definition;

means for searching at least one recommendation database;

means for retrieving rating data from at least one member of the community defined by the declarative community definition; and

means for generating at least one recommendation based at least in part on the rating data.

10. The apparatus of claim 9, further comprising:

means for receiving a user-defined item constraint; and

means for generating at least one recommendation based at least in part on the user-defined item constraint.

11. The apparatus of claim 9, wherein the means for generating at least one recommendation based at least in part on the rating data comprises:

means for generating at least one recommendation according to an aggregation function of item attributes from the rating data.

12. The apparatus of claim 9, further comprising:

means for progressively sampling the rating data according to a user-defined sampling policy.

13. The apparatus of claim 9, further comprising:

means for perturbing the rating data.

14. The apparatus of claim 9, further comprising:

means for receiving a user-defined recommendation algorithm; and

means for generating at least one recommendation based at least in part on the user-defined recommendation algorithm.

15. A computer readable medium storing computer program instructions for generating a recommendation, the computer program instructions defining the steps of:

receiving a user-defined recommendation request;

receiving a user-defined declarative community definition;

searching at least one recommendation database;

retrieving rating data from at least one member of the community defined by the declarative community definition; and

generating at least one recommendation based at least in part on the rating data.

16. The computer readable medium of claim 15, wherein the computer program instructions further comprise computer program instructions defining the steps of:

receiving a user-defined item constraint; and

generating at least one recommendation based at least in part on the user-defined item constraint.

17. The computer readable medium of claim 15, wherein the computer program instructions defining the step of generating at least one recommendation based at least in part on the rating data comprise computer program instructions defining the step of:

generating at least one recommendation according to an aggregation function of item attributes from the rating data.

18. The computer readable medium of claim 15, wherein the computer program instructions further comprise computer program instructions defining the step of:

progressively sampling the rating data according to a user-defined sampling policy.

19. The computer readable medium of claim 15, wherein the computer program instructions further comprise computer program instructions defining the step of:

perturbing the rating data.

20. The computer readable medium of claim 15, wherein the computer program instructions further comprise computer program instructions defining the steps of:

receiving a user-defined recommendation algorithm; and

generating at least one recommendation based at least in part on the user-defined recommendation algorithm.