DocumentCode :
465763
Title :
A Sequential Learning Algorithm for Collaborative Filtering With Linear Fuzzy Clustering
Author :
Honda, Katsuhiro ; Hidetomo, Ichihashi ; Notsu, Akira
Author_Institution :
Osaka Prefecture Univ., Osaka
Volume :
2
fYear :
2006
fDate :
8-11 Oct. 2006
Firstpage :
1056
Lastpage :
1061
Abstract :
Collaborative filtering is a technique for reducing information overload, and personalized recommendation is performed by predicting missing values in a data matrix. While the memory-based algorithms are widely used in conjunction with Web technology, the model-based algorithms are useful for estimating prediction models, in which we can predict missing values without holding all elements of the data matrix. Linear fuzzy clustering is a technique for local principal component analysis and can be used for estimating local prediction models considering data substructures. This paper proposes a new algorithm for estimating local linear models that performs a simultaneous application of fuzzy clustering and principal component analysis based on sequential subspace learning. In numerical experiments, the diagnostic power of the filtering system is shown to be improved by predicting missing values using the proposed local linear models.
Keywords :
filtering theory; fuzzy set theory; learning (artificial intelligence); principal component analysis; Web technology; collaborative filtering; data matrix; information overload reduction; linear fuzzy clustering; memory-based algorithms; personalized recommendation; principal component analysis; sequential learning algorithm; sequential subspace learning; Clustering algorithms; Collaboration; Filtering algorithms; Information filtering; Information filters; Nonlinear filters; Power filters; Power system modeling; Predictive models; Principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
1-4244-0099-6
Electronic_ISBN :
1-4244-0100-3
Type :
conf
DOI :
10.1109/ICSMC.2006.384539
Filename :
4273987
Link To Document :
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