Decision fusion of recommender scores through fuzzy aggregation connectives

• US 20030229896A1
• Filed: 06/10/2002
• Published: 12/11/2003
• Est. Priority Date: 06/10/2002
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Buczak, Anna L.
• Application Number:
  US10/165,932
• Publication Number:
  US 20030229896A1
• Time in Patent Office: Days
• US Class Current: 725/46
• 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 21/4661 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 7/163 by receiver means only

Abstract

A method of fusing recommender scores includes the steps of: (a) providing a first recommender score for a topic of interest based on a first set of information; (b) providing a second recommender score for the topic of interest based on a second set of information; (c) fusing the first recommender score and the second recommender score by compensatory fuzzy aggregation connectives; and (d) providing a final recommendation for the topic of interest based on the fusion in step (c). The method may include providing at least a third recommender score, and step (c) includes fusing the third recommender score with the first recommender score and the second recommender score. The final recommendation can be output on one of a display unit and a television set. The compensatory fuzzy aggregation connectives used for fusing in step (c) may include a Generalized Mean or a Gamma Model. The first and second recommender scores, while related to same topic, could be scores for different people, such as a couple watching television.

Claims

1. A method of fusing recommender scores, comprising the steps of:

(a) providing a first recommender score for a topic of interest based on one of a first set of information and a first method;

(b) providing a second recommender score for the topic of interest based on one of a second set of information and a second method;

(c) fusing the first recommender score and the second recommender score by compensatory fuzzy aggregation connectives; and

(d) providing a final recommendation for the topic of interest based on the fusion in step (c).

2. The method according to claim 1, wherein step (b) further comprises providing at least a third recommender score, and step (c) includes fusing said at least third recommender score with the first recommender score and the second recommender score.

3. The method according to claim 1, wherein the final recommendation is output on one of a display unit and a television set.

4. The method according to claim 1, wherein the compensatory fuzzy aggregation connectives used for fusing in step (c) comprises a Generalized Mean.

5. The method according to claim 4, wherein the Generalized Mean is determined according to the following equation:

6. The method according to claim 5, wherein the w_i'"'"'s are determined by the following equation:

7. The method according to claim 4, wherein:

controlling a rate of compensation for the Generalized Mean by changing the value of p so that when a value of p is increased, the operation becomes closer to a union.

8. The method according to claim 1, wherein the compensatory fuzzy aggregation connectives used for fusing in step (c) comprises a Gamma Model.

9. The method according to claim 6, wherein the Gamma model is determined according to the following equation:

10. The method according to claim 9, wherein the weights are determined by the following equation:

11. The method according to claim 8, further comprising controlling a rate of compensation for the Gamma Model by changing the value of γ

so that when a value of γ

is increased, the operation becomes closer to a union.

12. The method according to claim 2, wherein the compensatory fuzzy aggregation connectives used for fusing in step (c) comprises a Gamma Model.

13. The method according to claim 10, wherein the Gamma Model determined according to the following equation:

14. The method according to claim 1, wherein the first recommender score and the second recommender score comprise recommendations for one of television shows and movies.

15. The method according to claim 1, wherein the first recommender score and the second recommender score comprise recommendations for books.

16. The method according to claim 1, wherein the first recommender score and the second recommender score comprise recommendations for music.

17. The method according to claim 2, wherein the first recommender score, the second recommender score, and said at least third recommender score comprise recommendations for television shows.

18. The method according to claim 2, wherein the first recommender score, the second recommender score and the third recommender score comprise recommendations for one of books and music.

19. The method according to claim 1, wherein the first recommender score in step (a) is provided for a first person, and the second recommender score in step (b) is provided for a second person.

20. The method according to claim 2, wherein the first recommender score in step (a) is provided for a first person, and the second recommender score in step (b) is provided for a second person, and the third recommender score is provided for one of the first person and the second person.

21. The method according to claim 2, wherein the first recommender score in step (a) is provided for a first person, and the second recommender score in step (b) is provided for a second person, and the third recommender score is provided for a third person.