Method of and system for recommending programs

• US 20020157096A1
• Filed: 04/18/2002
• Published: 10/24/2002
• Est. Priority Date: 04/23/2001
• Status: Active Grant

• Current Assignee: NEC Personal Computers, Ltd. (Lenovo Group Ltd.)
• Original Assignee: NEC Corporation
• Inventors: Kamei, Shinichiro, Hane, Hidetaka
• Granted Patent: US 7,421,725 B2
• Time in Patent Office: Days
• US Class Current: 725/46
• CPC Class Codes: G06F 16/735 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: H04H 60/27 Arrangements for recording or accumulating broadcast information or broadcast-related information
• CPC Class Codes: H04H 60/31 Arrangements for monitoring the use made of the broadcast services
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04H 60/65 for using the result on users' side
• CPC Class Codes: H04N 21/4351 involving reassembling additional data, e.g. rebuilding an executable program from recovered modules
• 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 21/4622 Retrieving content or additional data from different sources, e.g. from a broadcast channel and the Internet web site content organization and management for information retrieval from the Internet G06F16/958; transmission by internet of broadcast information H04H60/82; stock exchange data over packet-switching network H04L12/1804; push services including data channel over packet-switching network H04L12/1859
• 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 21/4665 involving classification methods, e.g. Decision trees
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/8405 represented by keywords
• CPC Class Codes: H04N 7/163 by receiver means only

Abstract

Television programs are recommended in a flexible manner which a conventional keyword learning process has failed and also recommended with high accuracy. A method of recommending a program in an apparatus which receives broadcasts of programs together with information representing contents of the programs is carried out by generating a user profile having a plurality of themes with numerical values assigned thereto for representing preferred programs of the user, classifying themes by assigning numerical values to a plurality of respective themes with respect to each of a plurality of programs, using theme dictionaries containing keywords given to the themes, and determining programs to be recommended from the user profile and the numerical values assigned to the themes with respect to each of the programs.

Claims

1. A method of recommending a program in an apparatus which receives broadcasts of programs together with information representing contents of the programs, comprising the steps of:

generating a user profile having a plurality of themes with numerical values assigned thereto for representing preferred programs of the user;

classifying themes by assigning numerical values to a plurality of respective themes with respect to each of a plurality of programs, using theme dictionaries containing keywords given to the themes; and

determining programs to be recommended from said user profile and the numerical values assigned to the themes with respect to each of the programs.

2. A method according to claim 1, wherein said user profile is generated by classifying themes with respect to programs which have been preset in the past for timer recording by the user or programs which have been specified as preferred programs by the user.

3. A method according to claim 1, wherein themes are classified for generating said user profile, using unique numbers of keywords in the theme dictionaries contained in the information representing the contents of the programs, as points in the themes of the programs.

4. A method according to claim 1, wherein themes are classified for generating said user profile, using the total numbers of keywords in the theme dictionaries contained in the information representing contents of the programs, as points in the themes of the programs.

5. A method according to claim 1, wherein numerical values assigned to themes in the user profiles are added to the numerical values assigned to the corresponding themes of the programs, and the sum is used as a level of recommendation.

6. A method according to claim 1, further comprising the step of:

displaying a list of recommended programs together with the titles of themes most contributed to the recommendation of the programs.

7. A method according to claim 1, further comprising the step of:

individually generating a user profile of each of a plurality of users.

8. A method according to claim 1, wherein the information representing contents of the programs comprises EPG data.

9. A system for recommending a program in an apparatus which receives broadcasts of programs together with information representing contents of the programs, comprising:

user'"'"'s taste learning means for generating a user profile having a plurality of themes with numerical values assigned thereto for representing preferred programs of the user, and classifying themes by assigning numerical values to a plurality of respective themes with respect to each of a plurality of programs, using theme dictionaries containing keywords given to the themes; and

recommended program determining means for determining programs to be recommended from said user profile and the numerical values assigned to the themes with respect to each of the programs.

10. A system according to claim 9, wherein said user'"'"'s taste learning means generates said user profile by classifying themes with respect to programs which have been preset in the past for timer recording by the user or programs which have been specified as preferred programs by the user.

11. A system according to claim 9, wherein themes are classified for generating said user profile with said user'"'"'s taste learning means, using unique numbers of keywords in the theme dictionaries contained in the information representing the contents of the programs, as points in the themes of the programs.

12. A system according to claim 9, wherein themes are classified for generating said user profile with said user'"'"'s taste learning means, using the total numbers of keywords in the theme dictionaries contained in the information representing contents of the programs, as points in the themes of the programs.

13. A system according to claim 9, wherein said recommended program determining means adds numerical values assigned to themes in the user profiles to the numerical values assigned to the corresponding themes of the programs, and uses the sum as a level of recommendation.

14. A system according to claim 9, wherein said recommended program determining means displays a list of recommended programs together with the titles of themes most contributed to the recommendation of the programs.

15. A system according to claim 9, wherein said user'"'"'s taste learning means individually generates a user profile of each of a plurality of users.

16. A system according to claim 9, wherein the information representing contents of the programs comprises EPG data.