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

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Presentation on theme: "Smart Shopper A Consumer Decision Support System Using Type-2 Fuzzy Logic Systems Ling Gu 2003 Fall CSc8810."— Presentation transcript:

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 Processing Analyzer (a) Crisp Output Crisp Input

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]  P ,0 LowMediumHigh(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 Input Processing Output Processing Analyzer Defuzzifier (b) Crisp Output 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  Q10 1 0, 0 LowMediumHigh(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 0.5  x 1 1 0, 0  avg 1  1 0, x = 0.65 (b) (a) HH LL HH LL  avg Special case: type-2 membership function is an interval set that the secondary membership function is either zero or one

11 Proposed Approach (1) Algorithm  =  avg    L =  avg -   H =  avg +  (R) = (P) (Q) + (P) (Q) 5 Q 100, 0  1 LowMediumHigh  (Q) = 0.05 (R) = (P) (Q)

12 Proposed Approach (2) Type Reduce 5 Q 100, 0  1 LowMediumHigh (R) = (P) (Q avg ) (R) = (P) (Q avg ) + (P) (Q) = (P) (Q)  avg 5 Q 100, 0 1 LowMediumHigh

13 Proposed Approach (3)Results R = R avg  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  [Jang97] Jang, J-S. R., C-T. Sun, and E. Mizutani, "Neuro-Fuzzy and Soft Computing", Prentice Hall,  [Liang00] Liang, Q.L. and J.M. Mendel, "Interval Type-2 Fuzzy Logic System: Theory and Design", IEEE Transaction on Fuzzy System, 8, (2000).  [Kamik99] Kamik, N.N., J.M. Mendel, and Q.L. Liang, "Type-2 Fuzzy Logic System", IEEE Transaction on Fuzzy System, 7, (1999).  [Popp96] Popp, H. and D. Lodel, "Fuzzy Techniques and User Modeling in Sales Assistants", User Modeling and User-Adapted Interaction, 5, pp (1996).  [Roussinov01] Roussinov, D.G. and H-C. Chen, "Information navigation on the web by clustering and summarizing query results", Information Processing and Management, 37, pp (2001).  [Yager88] Yager, R.R., "On ordered weighted averaging aggregation operators in multicriteria decisionmaking", IEEE Transactions on systems, man and cybernetics: Part A, 18, pp (1988).  [Zadeh75] Zadeh, L.A., "The Concept of a Linguistic Variable and its Application to Approximate Reasoning - I", Information Sciences, 8, pp (1975).  [Zadeh01] Zadeh, L.A., "Letter to the members of the BISC group", in: Proceedings of the BISC Int. Workshop on Fuzzy Logic and the Internet,  [Zhang03] Zhang, Y-Q., "Fuzzy Logic, the Internet and E-Commerce", In: Fuzzy Web Shopping Agents, 2003.

21 DEMO


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