Method and apparatus for recommending items of interest based on preferences of a selected third party

• US 20030093329A1
• Filed: 11/13/2001
• Published: 05/15/2003
• Est. Priority Date: 11/13/2001
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Gutta, Srinivas
• Application Number:
  US10/014,202
• Publication Number:
  US 20030093329A1
• Time in Patent Office: Days
• US Class Current: 705/26.1
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• 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 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations, based on the viewing or purchase history of a selected third party. A viewing history of a selected third party is partitioned into a set of similar clusters. A given cluster corresponds to a segment of television programs exhibiting a specific pattern. A user can select one or more clusters from the clustered third party viewing history to supplement or replace corresponding portions (clusters) of the user'"'"'s own viewing history to produce a modified viewing history. The modified viewing history is processed to generate a user profile that characterizes the viewing preferences of the user, as well as the selected viewing preferences of the third party. Program recommendations are generated using the modified user profile.

Claims

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

obtaining a history of selecting one or more available items by at least one third party; and

generating a recommendation score for at least one of said available items based on said third party selection history.

2. The method of claim 1, further comprising the step of partitioning said third party selection history into clusters containing similar items.

3. The method of claim 2, wherein said obtaining step further comprises the step of receiving a user selection of one or more of said clusters of similar items.

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

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

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

7. A method for maintaining a user profile indicating preferences of a user, comprising the steps of:

partitioning a third party selection history into clusters containing similar items;

receiving a selection from said user of at least one of said clusters of similar items; and

updating said user profile using said selected clusters.

8. The method of claim 7, wherein said user profile is associated with a program content recommender.

9. The method of claim 8, wherein said user profile indicates viewing preferences of said user.

10. The method of claim 7, wherein said step of updating said user profile further comprises the steps of updating a selection history of said user with items from said selected clusters and updating said user profile using said updated selection history.

11. The method of claim 7, wherein said one or more items are programs.

12. The method of claim 7, wherein said one or more items are content.

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

14. A system for recommending one or more available items, comprising:

a memory for storing computer readable code; and

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

obtain a history of selecting one or more available items by at least one third party; and

generate a recommendation score for at least one of said available items based on said third party selection history.

15. The system of claim 14, wherein said processor is further configured to partition said third party selection history into clusters containing similar items.

16. The system of claim 15, wherein said processor is further configured to receive a user selection of one or more of said clusters of similar items.

17. A system for recommending one or more available items, comprising:

means for obtaining a history of selecting one or more available items by at least one third party; and

means for generating a recommendation score for at least one of said available items based on said third party selection history.

18. A system for maintaining a user profile indicating preferences of a user, comprising:

a memory for storing computer readable code; and

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

partition a third party selection history into clusters containing similar items;

receive a selection from said user of at least one of said clusters of similar items; and

update said user profile using said selected clusters.

19. The system of claim 18, wherein said user profile is associated with a program content recommender.

20. The system of claim 18, wherein said user profile indicates viewing preferences of said user.

21. The system of claim 18, wherein said step of updating said user profile further comprises the steps of updating a selection history of said user with items from said selected clusters and updating said user profile using said updated selection history.

22. An article of manufacture for recommending one or more available items, comprising:

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

a step to obtain a history of selecting one or more available items by at least one third party; and

a step to generate a recommendation score for at least one of said available items based on said third party selection history.

23. An article of manufacture for maintaining a user profile indicating preferences of a user, comprising:

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

a step to partition a third party selection history into clusters containing similar items;

a step to receive a selection from said user of at least one of said clusters of similar items; and

a step to update said user profile using said selected clusters.