1. Smart Shopper A Consumer Decision Support System Using Type-2 Fuzzy Logic Systems
Ling Gu
2003 Fall
CSc8810

2. Outline Decision Support System Why Fuzzy Logic System
Type-1 Fuzzy Logic Systems and membership function
Type-2 Fuzzy Logic Systems and membership function
Proposed Approach
Implementation
Discussion and Conclusion
Future Work

3. Decision support systems
The consumer decision support systems is to extract products that match users’ queries, and filter out unmatched products.
The match is measured by a ranking function.
The filtering function calculates the ranking of each product and filters out the lower ranked products.

4. Why Fuzzy Logic System
The fuzziness nature of the e-commerce makes the ranking process much more difficult.
User's queries are often complex and fuzzy.
They are contradictory and need to be balanced
The general framework of fuzzy reasoning allows handling of this uncertainty.

5. Type-1 Fuzzy Logic Systems
Type-1 fuzzy sets represent uncertainty by numbers in the range [0, 1].
Fuzzifier
Inference
Rules
Defuzzifier
Input
Processing
Output
Analyzer
(a)
Crisp

6. Type-1 Membership Function
Two-dimension in which each element of the type-1 fuzzy set has a membership grade that is a crisp number in [0, 1].
1000  P
2000 1
0,0
Low
Medium
High
(a)

7. Type-2 Fuzzy Logic Systems
Type-2 fuzzy sets are an extension of type-1 fuzzy sets in which uncertainty is represented by an additional dimension.
Crisp
Input
Fuzzifier
Inference
Rules
Type-Reducer
Processing
Output
Analyzer
Defuzzifier
(b)
Type
Reduced
Set

8. Type-2 Membership Function
Three dimensions in which each element of the type-2 fuzzy set has a membership grade that is a fuzzy set in [0, 1]. 5  Q 10 1
0, 0
Low
Medium
High
(b)

9. Advantages for Type 2 FLS
This extra third dimension in type-2 fuzzy logic systems (FLS) gives more degrees of freedom for better representation of uncertainty compared to type-1 fuzzy sets.
Type-2 fuzzy sets are useful in circumstances where it is difficult to determine the exact membership function for a fuzzy set.
Using type-2 FLS provides the capability of handling a higher level of uncertainty and provides a number of missing components that have held back successful deployment of fuzzy systems in human decision making.

10. Interval Type-2 Membership Function
Special case: type-2 membership function is an interval set that the secondary membership function is either zero or one
0.5  x 1
0, 0
avg
0.65
x = 0.65
(b)
(a) H L

11. Proposed Approach (1) Algorithm
 = avg   5 Q 10
0, 0  1
Low
Medium
High
(Q) = 0.05
L = avg - 
H = avg + 
(R) = (P) (Q)
(R) = (P) (Q) + (P) (Q)

12. Proposed Approach (2) Type Reduce5 Q 10
0, 0  1
Low
Medium
High
avg
Low
Medium
High 1 Q
0, 0 5 10
(R) = (P) (Qavg)
(R) = (P) (Qavg) + (P) (Q) = (P) (Q)

13. Proposed Approach (3)Results
R = Ravg  R

14. Implementation
Java servlet is used to implement this type-2 FLS-based consumer decision support system.
Two inputs: one (price) uses type-1, the other (quality) uses type-2.
The result (rank) is a fuzzy set and ranges from the low limit to the high limit.

15. Smart Shopper User Interface(1)

16. Smart Shopper User Interface(2)

17. Discussion
A better results might be obtained by defined the membership function of price also to be type-2.
It is important to define reasonable membership functions.
Using an interval input for the price, which provides more freedom for users.
Provide a weight function.

18. Conclusion
An up-low limit method has been proposed to handle the complex calculations of type-2 FLS.
This approach reduces the complex calculations of type-2 to type-1.
A fuzzy output of an interval type-2 FLS can be obtained using the up-low limit technique. This fuzzy output provides more reasonable conclusion for the users.

19. Future Work Use the generation of membership functions.
More type-2 variables.
Weight function.
Interval inputs to improve the system.

20. REFERENCES
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[Zhang03] Zhang, Y-Q., "Fuzzy Logic, the Internet and E-Commerce", In: Fuzzy Web Shopping Agents, 2003.

21. DEMO