RECOMMENDATION APPARATUS

• US 20090094285A1
• Filed: 10/01/2008
• Published: 04/09/2009
• Est. Priority Date: 10/03/2007
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Mackle, Edward G., Tarrant, Declan, Pilson, Keith, Quigley, Aaron, Phelan, Eamon
• Application Number:
  US12/243,136
• Publication Number:
  US 20090094285A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/40 of multimedia data, e.g. slideshows comprising image and additional audio data retrieval of still image data G06F16/50; retrieval of audio data G06F16/60; retrieval of video data G06F16/70
• CPC Class Codes: G06F 16/635 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/639 using playlists

Abstract

A recommendation apparatus for selecting, by comparing one or more media files from a plurality of received recommendations, one or more media files for inclusion into a digital media file. A recommendation apparatus in accordance with an embodiment includes: a main controller component for receiving a plurality of recommendations associated with one or more media files from a plurality of recommenders; a weighting component for determining a weighting factor associated with each of the received recommendations; a weighting component for identifying from each of the determined weighting factors each of the recommendations falling within a predetermined threshold and for selecting each of these recommendations for inclusion into an audio playlist.

Claims

1. A recommendation apparatus for selecting, by comparing one or more media files from a plurality of received recommendations, one or more media files for inclusion into a digital media file, the recommendation apparatus comprising:

a main controller component for receiving a plurality of recommendations associated with one or more media files from a plurality of recommenders;

a weighting component for determining a weighting factor associated with each of the received recommendations; and

a weighting component for identifying from each of the determined weighting factors each of the recommendations falling within a predetermined threshold, and for selecting each of these recommendations for inclusion into an audio playlist.

2. The recommendation apparatus as claimed in claim 1, wherein a weighting factor is representative of a conferred average rating associated with each of the plurality of recommenders and a highest number of recommendations received by any recommender.

3. The recommendation apparatus as claimed in claim 2, wherein a weighting factor is derived by mapping a set of rules against an organizational tree structure in order to determine a recommender'"'"'s rank in an organization.

4. The recommendation apparatus as claimed in claim 1, further comprising an audio generator component for identifying a selected media file as a text file.

5. The recommendation apparatus as claimed in claim 4, further comprising a text to speech converter for converting the identified text file into an audio file.

6. The recommendation apparatus as claimed in claim 1, further comprising a publication component for providing a list of all recommendations which have been selected for inclusion into a podcast and means for receiving a vote from a recommender based on a criterion for at least one of the selected recommendations.

7. The recommendation apparatus as claimed in claim 1, further comprising a subscription component for receiving a subscription to digital media file from a subscriber.

8. The recommendation apparatus as claimed in claim 1, wherein the digital media file is a podcast.

9. A method for selecting, by comparing one or more media files from a plurality of received recommendations, one or more media files for inclusion into a digital media file, the method comprising:

receiving a plurality of recommendations associated with one or more media files from a plurality of recommenders;

determining a weighting factor associated with each of the received recommendations; and

identifying from each of the determined weighting factors each of the recommendations falling within a predetermined threshold and selecting each of these recommendations for inclusion into an audio playlist.

10. The method as claimed in claim 9, wherein a weighting factor is representative of a conferred average rating associated with each of the plurality of recommenders and a highest number of recommendations received by any recommender.

11. The method as claimed in claim 10, wherein a weighting factor is further derived from mapping a set of rules against an organizational tree structure in order to determine a recommender'"'"'s rank in an organization.

12. The method as claimed in claim 9, further comprising identifying a selected media file as a text file.

13. A method as claimed in claim 12, further comprising converting the identified text file into an audio file.

14. The method as claimed in claim 9, further comprising providing a list of all recommendations which have been selected for inclusion into a podcast and receiving a vote from a recommender based on a criterion for at least one of the selected recommendations.

15. The method as claimed in claim 9, further comprising receiving a subscription to digital media file from a subscriber.

16. The method as claimed in claim 9, wherein the digital media file is a podcast.

17. A computer program product loadable into the internal memory of a digital computer, for selecting, by comparing one or more media files from a plurality of received recommendations, one or more media files for inclusion into a digital media file, when the program product is run on a computer, the program product comprising software code portions for:

receiving a plurality of recommendations associated with one or more media files from a plurality of recommenders;

determining a weighting factor associated with each of the received recommendations; and

identifying from each of the determined weighting factors each of the recommendations falling within a predetermined threshold and selecting each of these recommendations for inclusion into an audio playlist.