CONTEXT-BASED RECOMMENDER SYSTEM

1 . A system for generating a recommendation for at least one content item, the apparatus comprising:

a) a first extractor (S 200 ) for applying a first feature extraction algorithm to extract first features characterizing a content of a data input processed by a first application running on said system;

b) a second extractor (S 100 ) for applying a second feature extraction algorithm to extract second features characterizing a content of a database ( 32 ) of a second application running on said system; and

c) a comparator (S 300 ) for comparing said first and second features to identify matching items used for said recommendation;

d) wherein said first extractor (S 200 ) is adapted to detect whether said content of said data input relates to a television program or an existing film or television production.

2 . The system according to claim 1 , further comprising a switching functionality triggered by the first application so as to activate the second application.

3 . The system according to claim 1 , wherein said first application comprises an Internet browser ( 20 ) and said data input comprises content information downloaded from the Internet.

4 . The system according to claim 3 , wherein said content information comprises a HTML document.

5 . The system according to claim 1 , wherein said database ( 32 ) of said second application comprises electronic program guide information.

6 . The system according to claim 1 , wherein said database ( 32 ) of said second application is a movie database.

7 . The system according to claim 1 , wherein said first and second feature extraction algorithms are adapted to remove at least one of tags and stop words from said data input.

8 . The system according to claim 1 , wherein said comparator (S 300 ) is adapted to identify a matching item based on an amount of overlap between said first and second features.

9 . The system according to claim 1 , wherein said first and second features comprise vectors of term frequency inverse document frequency values.

10 . The system according to claim 1 , wherein said comparator is adapted to apply at least one of a word stemmer procedure, an approximate string matching procedure, and a procedure for calculating n-grams.

11 . The system according to claim 1 , wherein said first extractor (S 200 ) comprises an automatic keyword identifier for a webpage text, and wherein keywords are marked to be used to seed a personal television channel.

12 . The system according to claim 1 , wherein said second features comprise metadata provided in said database ( 32 ).

13 . The system according to claim 12 , wherein said comparator (S 300 ) is adapted to apply different weights to said metadata.

14 . The system according to claim 1 , wherein said second features comprise a Content Reference Identifier of a TV Anytime functionality.

15 . The system according to claim 1 , further comprising a user interface ( 22 ) for providing an input function for selecting said matching items.

16 . A method of generating a recommendation for at least one content item, the method comprising:

a) applying a first feature extraction algorithm to extract first features characterizing a content of a data input processed by a first data processing application;

b) applying a second feature extraction algorithm to extract second features characterizing a content of a database of a second data processing application; and

c) comparing said first and second features to identify matching items used for said recommendation;

d) wherein said first extractor (S 200 ) is adapted to detect whether said content of said data input relates to a television program or an existing film or television production.

17 . A computer program product comprising code means for producing the steps of method claim 16 when run on a computing device.