Method and apparatus for recommending items of interest to a user based on recommendations for one or more third parties

• US 7,571,452 B2
• Filed: 11/13/2001
• Issued: 08/04/2009
• Est. Priority Date: 11/13/2001
• Status: Expired due to Fees

• Current Assignee: Sisvel S.p.A. (Fineur International S.A.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Gutta, Srinivas
• Primary Examiner(s): Lonsberry; Hunter B.
• Application Number:
  US10/014,194
• Publication Number:
  US 20030093793A1
• Time in Patent Office: 2,821 Days
• Field of Search: 725 39- 46
• US Class Current: 725/46
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; 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 21/454 Content or additional data filtering, e.g. blocking advertisements
• 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 7/163 by receiver means only

Abstract

A method and apparatus are disclosed for recommending items of interest to a user based on recommendations made to one or more third parties. The recommendation scores generated by a primary recommender are influenced by recommendations generated for one or more third parties, such as a friend, colleague or trendsetter. The disclosed recommender corroborates with other recommenders when recommending items of interest and adjusts a conventional recommender score based on third party recommendations. The third party recommendations may be a top-N list of recommended items for a given third party, and may optionally include a recommendation score and an indication of whether or not the third party actually selected the recommended item. A recommender evaluates the viewing or purchase habits of a user and communicates with one or more other recommenders to determine the items that are being recommended by such other recommenders.

Claims

1. A method for recommending one or more available items to a user, comprising the steps of:

generating a user recommendation score for at least one of said available items that reflects a history of selecting said one or more items by said user;

receiving a selection of at least one third party recommender from said user;

selecting from said at least one selected third party recommender at least one third party recommendation for said at least one of said available items that reflects a history of selecting said one or more items by said at least one selected third party recommender;

generating a third party recommendation score for said at least one of said available items based on said selected third party recommendation; and

Using a computer or other calculating device to calculate an adjusted recommendation score for said at least one of said available items for said user, wherein said user recommendation score is adjusted based on said third party recommendation score.

2. The method of claim 1 wherein said generating a third party recommendation score step further comprises the step of averaging a plurality of third party recommendation scores for said at least one of said available items.

3. The method of claim 1, wherein said selecting step further comprises the step of receiving said third party recommendation score from a remote recommender.

4. The method of claim 3, wherein said received third party recommendation includes an indication of whether said corresponding recommended item was selected by said selected third party recommender.

5. The method of claim 1, wherein said one or more items are programs.

6. The method of claim 1, wherein said one or more items are content.

7. The method of claim 1, wherein said one or more items are products.

8. The method of claim 1, wherein said selected third party recommender is an individual.

9. A system for recommending one or more available items to a user, comprising:

a memory for storing computer readable code; and

a processor operatively coupled to said memory, said processor configured to;

generate a user recommendation score for at least one of said available items that reflects a history of selecting said one or more items by said user;

receive a selection of at least one third party recommender from said user;

select from said at least one selected third party recommender at least one third party recommendation for said at least one of said available items that reflects a history of selecting said one or more items by said at least one selected third party recommender;

generate a third party recommendation score for said at least one of said available items based on said selected third party recommendation; and

calculate an adjusted recommendation score for said at least one of said available items for said user, wherein said user recommendation score is adjusted based on said third party recommendation score.

10. The system of claim 9, wherein said processor is further configured to average a plurality of third party recommendation scores for said at least one of said available items

11. The system of claim 9, wherein said system receives said third party recommendation score from a remote recommender.

12. The system of claim 11, wherein said received third party recommendation includes an indication of whether said corresponding recommended item was selected by said selected third party recommender.

13. The system of claim 9, wherein said one or more items are programs.

14. The system of claim 9, wherein said one or more items are content.

15. The system of claim 9, wherein said one or more items are products.

16. The system of claim 9, wherein said selected third party recommender is an individual.

17. An article of manufacture for recommending one or more available items to a user, comprising:

a computer readable medium having computer readable code means embodied thereon, said computer readable program code means comprising;

a step to generate a user recommendation score for at least one of said available items that reflects a history of selecting said one or more items by said user;

a step to receive a selection of at least one third party recommender from said user;

a step to select from said at least one selected third party recommender at least one third party recommendation for said at least one of said available items that reflects a history of selecting said one or more items by said at least one selected third party recommender;

a step to generate a third party recommendation score for said at least one of said available items based on said selected third party recommendation; and

a step to calculate an adjusted recommendation score for said at least one of said available items for said user, wherein said user recommendation score is adjusted based on said third party recommendation score.