Collaborative sampling for implicit recommenders

• US 8,682,890 B2
• Filed: 11/30/2004
• Issued: 03/25/2014
• Est. Priority Date: 12/03/2003
• Status: Active Grant

• Current Assignee: ARRIS Enterprises LLC (CommScope Holding Company, Inc.)
• Original Assignee: Pace Micro Technology Ltd. (CommScope Holding Company, Inc.)
• Inventors: Gutta, Srinivas, Meuleman, Petrus Gerardus, Verhaegh, Wilhelmus Franciscus Johannes
• Primary Examiner(s): Saeed, Usmaan
• Assistant Examiner(s): VO, CECILE H
• Application Number:
  US10/596,165
• Publication Number:
  US 20070162440A1
• Time in Patent Office: 3,402 Days
• Field of Search: 707/5, 707/4, 707/7, 707/726, 707/758, 707/791, 725 10- 46, 705/14, 715716-719
• US Class Current: 707/726
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• 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 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4755 for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4756 for rating content, e.g. scoring a recommended movie
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

A method for generating a recommendation is provided. The method includes the steps of: receiving at least one of a negative and positive example from one or more other users; and determining a recommendation for a user based on at least one of the negative and positive examples. The method can further include the step of generating a user profile for the user based on previous behavior of the user, in which case the determining step can include determining the recommendation based on the negative example and the user profile. The determining step can also include the step of determining the recommendation based on both of the negative and positive examples.

Claims

1. A method for generating a recommendation of at least one television program for a viewer, the method comprising:

using the viewer'"'"'s television program recommender to access a plurality of user'"'"'s recommenders at locations remote from that at which the viewer is located;

using the viewer'"'"'s recommender to communicate with a selection of the user'"'"'s recommenders to generate data representing, at least one of a negative and positive example feedback from one or more selected other users recommenders to be received; and

generating a recommendation for at least one television program to be watched in the future by the viewer based on at least one of the received negative and positive examples feedback;

said generation of the recommendation being performed at the viewer location utilizing a processor provided as part of the viewer'"'"'s television program recommender.

2. The method of claim 1, further comprising generating a user profile for the user based on previous behavior of the user, wherein the determining comprises determining the recommendation based on the negative example and the user profile.

3. The method of claim 2, wherein the generating of the user profile comprises generating an implicit user profile.

4. The method of claim 1, wherein the determining comprises determining the recommendation based on the negative and positive examples.

5. The method of claim 1, further comprising:

mapping the negative example to an electronic program guide database of the user; and

determining an equivalent negative example for the user from the electronic program database.

6. The method of claim 1, further comprising:

mapping the positive example to an electronic program guide database of the user; and

determining an equivalent positive example for the user from the electronic program database.

7. The method of claim 1, further comprising determining the one or more other users.

8. The method of claim 7, wherein the determining comprises selecting the one or more other users based on geographical location of the one or more other users.

9. The method of claim 7, wherein the determining comprises selecting the one or more other users based on a similarity of likes and/or dislikes with the one or more other users.

10. The method of claim 7, wherein the determining comprises selecting the one or more other users by the user.

11. The method of claim 10, wherein the selecting comprises:

presenting a plurality of other users to the user; and

the user selecting from among the plurality of other users to determine the one or more other users.

12. The method of claim 11, wherein the presenting comprises presenting the plurality of users based on geographical location of the one or more other users.

13. The method of claim 11, wherein the presenting comprises presenting the plurality of users based on a similarity of likes and/or dislikes of the one or more other users.

14. The method of claim 7, wherein the selecting comprises the user indicating the one or more other users.