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 Presented By : Ren-De Ou Data : 2004/01/13

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

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

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

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

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 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