Method and apparatus for generating recommendations for a plurality of users

• US 20020174428A1
• Filed: 03/28/2001
• Published: 11/21/2002
• Est. Priority Date: 03/28/2001
• Status: Abandoned Application

• Current Assignee: Philips Electronics North America Corporation (Koninklijke Philips N.V.)
• Original Assignee: Philips Electronics North America Corporation (Koninklijke Philips N.V.)
• Inventors: Gutta, Srinivas, Agnihotri, Lalitha
• Application Number:
  US09/819,440
• Publication Number:
  US 20020174428A1
• Time in Patent Office: Days
• US Class Current: 725/46
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/44218 Detecting physical presence or behaviour of the user, e.g. using sensors to detect if the user is leaving the room or changes his face expression during a TV program methods or arrangements for recognising human body or animal bodies or body parts G06V40/10; methods or arrangements for acquiring or recognising human faces, facial parts, facial sketches, facial expressions G06V40/16; methods or arrangements for recognising movements or behaviour G06V40/20; arrangements for identifying users in broadcast systems H04H60/45
• 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 21/466 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 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/4751 for defining user accounts, e.g. accounts for children
• CPC Class Codes: H04N 7/163 by receiver means only

Abstract

A recommendation system is disclosed that generates recommendations for one or more items based on the combined preferences of a number of individuals. The disclosed recommender initially identifies the individuals that are present, and thereafter generates a recommendation score based on the combined preferences of each user. In one implementation, a recommendation score is first computed for each individual, before a combined recommendation score is computed for the entire group. The combined recommendation score, C, can be computed, for example, using an average or a weighted average.

Claims

1. A method for recommending an item to a group of users, comprising the steps of:

identifying said group of users; and

generating a recommendation score for said item based on features of said item and preferences of each of said users.

2. The method of claim 1, wherein said item is a program.

3. The method of claim 1, wherein said item is content.

4. The method of claim 1, wherein said item is a product.

5. The method of claim 1, wherein said recommendation score is computed as a weighted average of individual recommendation scores indicating a degree to which said item is likely to be of interest to each of said users.

6. The method of claim 1, wherein said recommendation score is computed using a straight average of individual recommendation scores indicating a degree to which said item is likely to be of interest to each of said users.

7. The method of claim 1, wherein said recommendation score is computed by analyzing a profile for said group of users indicating individual preferences of each of said users.

8. A method for recommending an item to a group of users, comprising the steps of:

identifying said group of users;

generating an individual recommendation score for said item for each of said users, said individual recommendation scores based on features of said item and preferences of said corresponding user; and

generating a combined recommendation score for said item based on said individual recommendation scores.

9. The method of claim 8, wherein said item is a program.

10. The method of claim 8, wherein said item is content.

11. The method of claim 8, wherein said item is a product.

12. The method of claim 8, wherein said combined recommendation score is computed as a weighted average of said individual recommendation scores.

13. The method of claim 8, wherein said combined recommendation score is computed using a straight average of said individual recommendation scores.

15. The system of claim 14, wherein said recommendation score is computed as a weighted average of individual recommendation scores indicating a degree to which said item is likely to be of interest to each of said users.

16. The system of claim 14, wherein said recommendation score is computed using a straight average of individual recommendation scores indicating a degree to which said item is likely to be of interest to each of said users.

17. The system of claim 14, wherein said recommendation score is computed by analyzing a profile for said group of users indicating individual preferences of each of said users.

19. The system of claim 18, wherein said combined recommendation score is computed as a weighted average of said individual recommendation scores.

20. The system of claim 18, wherein said combined recommendation score is computed using a straight average of said individual recommendation scores.

14. A system for recommending an item to a group of users, comprising:

a memory for storing computer readable code; and

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

identify said group of users; and

generate a recommendation score for said item based on features of said item and preferences of each of said users.

18. A system for recommending an item to a group of users, comprising:

a memory for storing computer readable code; and

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

identify said group of users;

generate an individual recommendation score for said item for each of said users, said individual recommendation scores based on features of said item and preferences of said corresponding user; and

generate a combined recommendation score for said item based on said individual recommendation scores.

21. An article of manufacture for recommending an item to a group of users, comprising:

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

a step to identify said group of users; and

a step to generate a recommendation score for said item based on features of said item and preferences of each of said users.

22. An article of manufacture for recommending an item to a group of users, comprising:

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

a step to identify said group of users;

a step to generate an individual recommendation score for said item for each of said users, said individual recommendation scores based on features of said item and preferences of said corresponding user; and

a step to generate a combined recommendation score for said item based on said individual recommendation scores.