Content recommendation device with an arrangement engine

• US 20050021420A1
• Filed: 06/04/2004
• Published: 01/27/2005
• Est. Priority Date: 06/04/2003
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: RealNetworks GmbH (RealNetworks, Inc.)
• Inventors: Schramm, Balthasar, Riegler, Andreas, Spechtler, Andreas, Zich, Otto, Petro, Oliver, Michelitsch, Stefan, Nemetz, Thomas, Burgstaller, Rainer
• Granted Patent: US 7,716,220 B2
• Time in Patent Office: Days
• US Class Current: 705/26
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Content recommendation device with an arrangement engine The invention relates to a content recommendation device comprising a selection engine (2) that selects content items from a content item pool (4) and generates a selection list (9) comprising the selected content items, and/or means to receive a selection list The content recommendation device also comprises an arrangement engine (3) that generates a content recommendation list (12) by re-arranging at least a part of content items of the selection list (9) so that preferably a dramaturgic sequence of content items is generated.

Claims

1. Content recommendation device, comprising

a selection engine (2) that selects content items from a content item pool (4) and generates a selection list (9) comprising the selected content items, and/or

means to receive a selection list,

characterized by

an arrangement engine (3) that generates a content recommendation list (12) by re-arranging at least a part of content items of the selection list (9).

2. Content recommendation device according to claim 1, characterized in that said arrangement engine (3) generates said content recommendation list (12) based on arrangement rules (7) that generate a dramaturgic sequence of content items.

3. Content recommendation device according to claim 1, characterized in that said arrangement engine (3) generates said content recommendation list (12) based on item attributes (5) that define every content item in a standardized way.

4. Content recommendation device according to claim 2, characterized in that said arrangement rules (7) combine individual item attributes (5).

5. Content recommendation device according to claim 1, characterized in that said arrangement engine (3) generates said content recommendation list (12) based on business rules (7) that generate a commercially influenced sequence of content items.

6. Content recommendation device according to claim 1, characterized in that said selection engine (2) generates said selection list (9) based on item attributes (5) that define every content item in a standardized way.

7. Content recommendation device according to claim 1, characterized in that said selection engine (2) generates said selection list (9) based on business rules (7) that generate a commercially influenced sequence of content items.

8. Content recommendation device according to claim 1, characterized in that said arrangement engine (3) generates said content recommendation list (12) based on a user profile (11).

9. Content recommendation device according to claim 1, characterized in that said selection engine (2) selects the content items from a content item pool (4) according to a user profile (11).

10. Content recommendation device according to claim 1, characterized by

a user profiling engine (1) that generates a user profile (11) based on a user feedback (10) to at least one previously output recommendation list (12).

11. Content recommendation device according to claim 10, characterized in that said user feedback (10) comprises explicit and implicit feedback.

12. Content recommendation device according to claim 11, characterized in that said user feedback (10) is a function of a representation of the explicit feedback weighted with a representation of the implicit feedback.

13. Content recommendation device according to claim 11, characterized in that said explicit feedback comprises an indication whether a user likes or dislikes a particular content item.

14. Content recommendation device according to claim 11, characterized in that said implicit feedback is based on the level of reaction accompanying the explicit feedback.

15. Content recommendation device according to claim 14, characterized in that said implicit feedback is based on the intensity the user is operating an input terminal of the content recommendation device.

16. Content recommendation device according to claim 14, characterized in that said implicit feedback comprises a pressure put on an explicit feedback control.

17. Content recommendation device according to claim 14, characterized in that said implicit feedback comprises a reaction time of a user to operate an explicit feedback control.

18. Content recommendation device according to claim 8, characterized in that said user feedback (10) is used to generate an iteratively refined recommendation list (12) on basis of a reduced content item pool that comprises the content items of the selection list (9), or a new selection list (9).

19. Method for content recommendation, comprising the steps

selecting content items from a content item pool (4) and generating a selection list (9) comprising the selected content items, and/or

receiving a selection list

characterized by

generating a content recommendation list (12) by re-arranging at least a part of content items of the selection list (9).

20. Method according to claim 19, characterized by generating said content recommendation list (12) based on arrangement rules (7) that generate a dramaturgic sequence of content items.

21. Method according to claim 19, characterized by generating said content recommendation list (12) based on item attributes (5) that define every content item in a standardized way.

22. Method according to claim 20, characterized by combining individual item attributes (5) with said arrangement rules (7).

23. Method according to claim 19, characterized by generating said content recommendation list (12) based on business rules (7) that generate a commercially influenced sequence of content items.

24. Method according to claim 19, characterized by generating said selection list (9) based on item attributes (5) that define every content item in a standardized way.

25. Method according to claim 19, characterized by generating said selection list (9) based on business rules (7) that generate a commercially influenced sequence of content items.

26. Method according to claim 19, characterized by generating said content recommendation list (12) based on a user profile (11).

27. Method according to claim 19, characterized by selecting the content items from a content item pool (4) according to a user profile (11).

28. Method according to claim 19, characterized by generating a user profile (11) based on a user feedback (10) to at least one previously output recommendation list (12).

29. Method according to claim 28, characterized in that said user feedback (10) comprises explicit and implicit feedback.

30. Method according to claim 29, characterized in that said user feedback (10) is a function of a representation of the explicit feedback weighted with a representation of the implicit feedback.

31. Method according to claim 29, characterized in that said explicit feedback comprises an indication whether a user likes or dislikes a particular content item.

32. Method according to claim 29, characterized in that said implicit feedback is based on the level of reaction accompanying the explicit feedback.

33. Method according to claim 32, characterized in that said implicit feedback is based on the intensity the user is operating an input terminal of the content recommendation device.

34. Method according to claim 32, characterized in that said implicit feedback comprises a pressure put on an explicit feedback control.

35. Method according to claim 32, characterized in that said implicit feedback comprises a reaction time of a user to operate an explicit feedback control.

36. Method according to claim 28, characterized in that said user feedback (10) is used to generate an iteratively refined recommendation list (12) on basis of a reduced content item pool that comprises the content items of the selection list (9), or a new selection list (9).

37. Computer program product, comprising computer program means adapted to perform the method steps as defined in claim 19 when being executed on a computer, digital signal processor or the like.

38. Computer readable storage means, storing thereon a computer program product according to claim 37.