1. CYUT ISKM 2004/01/13 1 Fuzzy logic methods in recommender systems Author: Ronald R. Yager Source:Fuzzy set and systems, Vol. 134, 2003, pp. 133-149 Presented By : Ren-De Ou Data : 2004/01/13

2. 2 CYUT ISKM 2004/01/13 Outline Introduction Related Work User profiles Object representation Recommend object Conclusions Comments

3. 3 CYUT ISKM 2004/01/13 Introduction Obtaining new customer and retaining existing ones. Electronic businesses try apply web personalization technique Use past behavior and inference from other similar user to service they like web object.

4. 4 CYUT ISKM 2004/01/13 Introduction Most use  Content-based approach  Collaborative filtering approach Use a new algorithm is Reclusive method to solution.

5. 5 CYUT ISKM 2004/01/13 Related Work Collaborative filtering  Base on correlation coefficients of users  Can use process multimedia data. Reclusive method  Base on single individual for we are providing the recommendation  make no use of the preferences of other collaborators

6. 6 CYUT ISKM 2004/01/13 MOVIE Star is Robert DeNiro Make at U.S.A Horror film Preference?? We use a nature represent his preference Such as a little, some, very….

7. 7 CYUT ISKM 2004/01/13 User profiles BPM: basic preference module  Assertion: state of object,such as horror film  Attribute: abstract assertion,such as type of movie  representation: valuation of set of assertion,such as movie Profile: set of preference representation

8. 8 CYUT ISKM 2004/01/13 profile representation assertions C is components

9. 9 CYUT ISKM 2004/01/13 Object representation OWA operator by yager(1988) Ordered weighted averaging(OWA) operator Preference dependent on  Assertions used to represent the object  Use language available to express their preferences In common use multi criteria decision making and nature language identification

10. 10 CYUT ISKM 2004/01/13 OWA operator  a i is arguments  w j is weighting  b j is the j th largest of the a i.

11. 11 CYUT ISKM 2004/01/13 OWA operator Example V=[0.2, 0.3, 0.1, 0.4]  Compute F(0.6, 1, 0.3, 0.5) F(0.6, 1, 0.3, 0.5)= V*B= [0.2, 0.3, 0.1, 0.4]*[1.0, 0.6, 0.5, 0.3] = 0.55  Compute F(0.0, 0.7, 1.0, 0.4) F(0.6, 1, 0.3, 0.5)= V*B= [0.2, 0.3, 0.1, 0.4]*[1.0, 0.7, 0.4, 0.0] = 0.43

12. 12 CYUT ISKM 2004/01/13 Recommend object Recommend an object if there exist a similar object that the user liked. Use representation to compute similarity S

13. 13 CYUT ISKM 2004/01/13 R is strength of recommend d i is an unexperienced object

14. 14 CYUT ISKM 2004/01/13 Conclusions Reclusive approaches differ form the CF  Based on preferences of the individual  Providing the recommendation need not preferences of other individuals. Reclusive method require a representation. Integration CF and reclusive method.

15. 15 CYUT ISKM 2004/01/13 Comments Assertions, attribute need expert setup Profile too complex, we can try to simplify Because all object has several assertions, so we can use combine syntax search target user