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CYUT Information Management
Inference of Recommendation Information on the Internet Using
Improved FAM
Authors: Won Kim, Il-Ju Ko, Jin-Sung Yoon, Gye-Young Kim
Source: Future Generation Computer Systems, Vol. 20, No.2, pp. 265-273, Feb. 2004
Instructor Professor:
Dr. Rong-Chang Chen
Presented By: Yu-Chun Chen (陳育純 )Wan-Jing Liu (劉宛晶 )
Date: 2005/01/12
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OutlineIntroductionIFAM Inferring Model Learning Model
ExperimentalConclusionsComment
Introduction ExperimentalIFAM ConclusionComment
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Introduction
FAM (Fuzzy Associative Memory) Fusion of associative memory and
fuzzy logic Inferring model and learning modelIFAM (Improved FAM) Deleted unimportant rulesInformation Overload Collaborative filtering system
Introduction ExperimentalIFAM ConclusionComment
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System organizationIntroduction ExperimentalIFAM ConclusionComment
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Input Interface ModuleIntroduction ExperimentalIFAM ConclusionComment
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CYUT Information ManagementRecommendation Interface Module
Introduction ExperimentalIFAM ConclusionComment
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CYUT Information ManagementFuzzy Associative Memory (1/3)
Introduction ExperimentalIFAM ConclusionComment
input association
output
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CYUT Information ManagementFuzzy Associative Memory (2/3)
A W = B A is a fuzzy set denote a max-min composition operator W denote weight B is a recommendation set
bj = maxi {min(ai ,wij)} b1 = max{min(0.8,0.2),min(0.5,0.6),
min(0.6,0.3),min(0.3,0.4)} = 0.5
Introduction ExperimentalIFAM ConclusionComment
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CYUT Information ManagementFuzzy Associative Memory (3/3)
Introduction ExperimentalIFAM ConclusionComment
input association
output
0.8
0.5
0.6
0.3
0.2
0.6
0.3
0.4
0.3
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Inference model with IFAMIntroduction ExperimentalIFAM ConclusionComment
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Membership Function
Divide the entire range into three parts and assign then with low, medium, high fuzzy sets
0.1
0.8 low
0.7
0.4 high
0.33 medium
Introduction ExperimentalIFAM ConclusionComment
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D value
The degree of usefulness of input fuzzy setsM : the number of recommendation itemsK : the total number of learning dataPAl(j) : the probability density of rates on the j th recommendation itemrBj(xi) : the rate of xi on a recommendation itemAi(xi) : the fit value of xi to an input fuzzy set Ai
Introduction ExperimentalIFAM ConclusionComment
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W value
Hebbian-style learning method η: positive learning rateai (n) : input associate for the n th learning databj (n) : output associate for the n th learning data
Introduction ExperimentalIFAM ConclusionComment
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Example
Price
Size
Branch
0.3
0.5
0.6
0.4
0.33
1
0.67
0.2
30,000 ∩ 2.2 KG ∩ 600
30,000 ∩ 2.2 KG ∩ 800
40,000 ∩ 2.2 KG ∩ 600
40,000 ∩ 2.2 KG ∩ 800
Item 1
Item 2
Item 3
Introduction ExperimentalIFAM ConclusionComment
D
W30,000
40,000
500
2.2 KG
600
50,000
2.4 KG
2.0 KG
800
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Experimental Results (1/3)Introduction ExperimentalIFAM ConclusionComment
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Experimental Results (2/3)Introduction ExperimentalIFAM ConclusionComment
(MAE: Mean Absolute Error)
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Experimental Results (3/3)Introduction ExperimentalIFAM ConclusionComment
(OMAE: Overall Mean Absolute Error)
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Conclusions
Provide reliable recommendationsRecommending high-quality informationUse for any userSimplified the fuzzy rules
Introduction ExperimentalIFAM ConclusionComment
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Comments
The performance of IFAM is not better than FAMThe sparsity problem
Introduction ExperimentalIFAM ConclusionComment
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Thanks for your listening
